SINGLE-NEURON THEORY OF CONSCIOUSNESS

 

Journal of Theoretical Biology (2005, In Press)

 

Steven Sevush

 

Departments of Psychiatry and Neurology

University of Miami School of Medicine

1400 NW 10 Ave, Suite 702

Miami, Florida 33136, USA

ssevush@med.miami.edu

Tel: 305-243-4082

 

This work was presented in abstract form at

"Toward a Science of Consciousness" held in Tucson, April 2002.

 

ABSTRACT:

 

     By most accounts, the mind arises from the integrated activity of large populations of neurons distributed across multiple brain regions.  A contrasting model is presented in the present paper that places the mind/brain interface not at the whole brain level but at the level of single neurons.  Specifically, it is proposed that each neuron in the nervous system is independently conscious, with conscious content corresponding to the spatial pattern of a portion of that neuron's dendritic electrical activity.  For most neurons, such as those in the hypothalamus or posterior sensory cortices, the conscious activity would be assumed to be simple and unable to directly affect the organism's macroscopic conscious behavior.  For a subpopulation of layer 5 pyramidal neurons in the lateral prefrontal cortices, however, an arrangement is proposed to be present such that, at any given moment:  i) the spatial pattern of electrical activity in a portion of the dendritic tree of each neuron in the subpopulation individually manifests a complexity and diversity sufficient to account for the complexity and diversity of conscious experience; ii) the dendritic trees of the neurons in the subpopulation all contain similar spatial electrical patterns; iii) the spatial electrical pattern in the dendritic tree of each neuron interacts nonlinearly with the remaining ambient dendritic electrical activity to determine the neuron's overall axonal response; iv) the dendritic spatial pattern is reexpressed at the population level by the spatial pattern exhibited by a synchronously firing subgroup of the conscious neurons, thereby providing a mechanism by which conscious activity at the neuronal level can influence overall behavior.  The resulting scheme is one in which conscious behavior appears to be the product of a single macroscopic mind, but is actually the integrated output of a chorus of minds, each associated with a different neuron.

 

Key Words:  binding problem; consciousness; hard problem; neural correlate of consciousness; single-neuron theory.

 

1. INTRODUCTION

 

     The purpose of this paper is to suggest a shift in emphasis from the large to the small in the search for a brain correlate for the mind.  Currently, the general view is that the mind arises from the integrated activity of large populations of neurons distributed across multiple cortical and subcortical brain regions (Sperry 1969; Crick and Koch 1990a; Llinas et al. 1998; Damasio 1999, Edelman and Tononi 2000, John 2001, Singer 2001).  Dynamic mechanisms, such as those involving reentrant flow of information (Edelman and Tononi 200), synchronous electrical oscillatory activity (Singer 1998; Engel et al. 1999; Sauve 1999; Sewards and Sewards 2001), and top-down attentional effects (Reynolds and Desimone 1999; Wolfe and Cave 1999), have then been invoked to provide for the "binding" of the dispersed neuronal activity into a unified stream of consciousness.  The perspective, according to this view, is that the activity within any single neuron correlates with merely a fragment of the total conscious experience; it is only through the integration of these fragments that a single whole-brain consciousness is assumed to emerge.

 

     A contrasting model is outlined in the present paper that places the mind/brain interface not at the whole brain level but at the level of the single neuron.  According to the model, a single brain at any given moment harbors many separate conscious minds, each being associated with the activity of a different neuron.  For most neurons, only a simple conscious experience is assumed to be present.  For a subpopulation of neurons populating the lateral prefrontal cortices, however, a complex consciousness is assumed to be separately present in each neuron, with each of the neurons having a similar conscious experience, and with that conscious experience being that which in most models is attributed to the joint action of vast numbers of neurons distributed throughout the brain.  The theory posits a mechanism that explains how the separately conscious neurons might express their output in terms of whole brain behavior.  The resulting scheme is one in which conscious behavior, while appearing to be the product of a single macroscopic mind, is actually the result of the assembled output of a chorus of minds, each associated with a different individual neuron.

 

     The main features of the theory can be summarized as follows:

 

     a) Each neuron in the nervous system is assumed to be independently conscious, with a component of the electrical activity in the neuron's dendritic tree serving as the neural correlate of consciousness (NCC) for that neuron.

 

    b) For most neurons, such as those in the hypothalamus or those in the posterior sensory cortices, or for cortical interneurons, the conscious activity of the neuron is assumed to be simple and unable to directly affect the organism's macroscopic behavior.  Such neurons would not, therefore, contribute to what is usually taken as a person’s conscious behavior.

 

      c) For layer 5 pyramidal neurons in the lateral prefrontal cortices, however, the electrical activity within a portion of the apical dendritic tree is presumed to be by itself complex and diverse enough to account for the complexity and diversity of conscious experiences usually ascribed to the activity of the brain as a whole.  Additionally, as a result of the diversity of afferent input that uniquely characterizes the lateral prefrontal cortices, the layer 5 pyramidal neurons in these areas can plausibly be regarded as recipients of input pertaining to all of the sensations, thoughts, feelings, and memories that make up conscious experience.

 

     d) A mechanism can be delineated by which a subgroup of synchronously firing layer 5 lateral PFC neurons with similar conscious experiences can express that experience by means of the spatial pattern they manifest at the neuronal population level.  The axonal output of these neurons then transmits the conscious content to other brain regions, including those that control behavior.

 

     e) As a result, while the content of conscious experience is seen to correlate with activity in individual neurons, the production of conscious behavior results from population activity at the network level.

 

     The most immediate implication of this arrangement is that it offers a novel approach to solving the "binding problem," the problem of explaining how spatially dispersed neuronal activity can correlate with an apparently seamless single experience.  The currently popular views are that either:  a) perceptual unity is achieved through temporal synchrony of the component elements, perhaps via 40 Hz gamma frequency oscillations (Singer 1998; Engel et al. 1999; Sauve 1999; Sewards and Sewards 2001); or b) perceptual unity is an illusion, with only coordinated behavioral output being in need of an explanation (Dennett 1991; Shadlen and Movshon 1999).  These approaches have their difficulties, however.  The evidence for temporal synchrony as a basis for binding has been criticized on both technical and conceptual grounds (Hardcastle 1994, 1997; Gold 1999; Revonsuo 1999; Bieberich 2002; Edwards 2005), while the argument that perceptual unity is an illusion has been challenged both empirically and philosophically (Chalmers 1996; Robertson 2003; Bayne and Chalmers 2003).  The theory presented in this paper proposes an alternative explanation for perceptual binding, with perceptual unity being achieved through spatial convergence of incoming signals upon single neurons.

 

     In what follows I present an outline of the single-neuron model and argue for its feasibility given what we know of brain architecture and function at both the macroscopic and microscopic levels.  I will then compare the model with other popular theories of consciousness and describe the empirical predictions made by the model that might adjudicate between the competing perspectives.  I will conclude by offering a brief speculation as to how the model might be extended to an ultramicroscopic domain, and how this extension might be used to formulate a framework for addressing the philosophically difficult aspects of the mind/brain problem.

 

     The model, it should be stressed, is at this point presented as a hypothesis, not a proven theory.  The contention, given the data presently available, is not that the proposed single-neuron mechanism is necessarily the way the brain processes information, only that it is a possible way for it to accomplish the task and that it should not be dismissed out of hand.  There is no question that the theory takes a stance that is at odds with prevailing intuitions.  Yet, the stakes are high and, in view of the difficulties inherent in the mind/brain problem, unusual approaches to its solution may be warranted.

 

2. TERMINOLOGY AND THE POSSIBILITY OF MULTIPLE CONSCIOUSNESSES

 

     With regard to terminology, when the terms "mind" and "consciousness" appear in this paper, they are being used in the specific sense that Chalmers uses the term "experience" in his discussion of the easy and hard problems of consciousness (Chalmers 1996).  As described by Chalmers, the "easy" problems of consciousness are those that appear tractable by the usual methods of science and include the ways in which the brain focuses attention, integrates information, controls behavior, and so on.  In contrast, the "hard" problem of consciousness is the problem of accounting for the content of subjective experience, that elusive datum that appears to accompany certain high level brain processes.  Why does the electrical processing in the brain that ensues when one looks at the sky give rise to the experience of "blue" and not to the experience of some other color, or to no color experience at all?  In what follows, it will be to consciousness in this sense that a neural correlate will be sought.

 

     An additional matter is the potential ambiguity associated with the proposal that multiple minds are present in a single brain, a feature that plays centrally in the proposed theory at both the macroscopic and microscopic levels.  The problem is that since multiple consciousnesses are assumed to be present in a single brain, and since these include consciousnesses of varying types, how will we know what is to be denoted by the term "consciousness?"  That is, to which of the different kinds of consciousness will the term refer?

 

     In order to minimize the potential for ambiguity of this kind, the theory will initially be developed for the restricted case of verbally reportable consciousness (designated in this paper as "VR-consciousness"), by which I mean consciousness that has direct access to verbal report.  It must be stressed that this restriction is a temporary one, and will be introduced only as a convenience to minimize ambiguity in the initial presentation of the theory.  It is not implied that VR-consciousness is the only type of consciousness present in the brain, nor that VR-consciousness is even the only high level consciousness present in the brain.  Nor is it implied that verbal output is the only form of behavior that can be influenced by VR-consciousness.  VR-consciousness has been chosen for this purpose because it is around this form of consciousness that the debate over the hard problem of consciousness has been primarily focused.  Once the theory has been examined for the special case of VR-consciousness, the argument will then be expanded to account for the presence of other types of consciousness.

 

     Armed with these preliminaries, we can now turn to the presentation of the model.  A sketch of the single-neuron theory for VR-consciousness will be developed in two steps, one macroscopic, the other microscopic.  At both levels, a divergent/convergent feedforward model of information flow (Abeles 1991) will be adopted.  In the macroscopic step, the suggestion will be made that within a divergent/convergent feedforward framework, much of the distributed brain processing usually taken as the correlate of VR-consciousness can be recast as either previous or subsequent to VR-conscious experience.  Plausibility arguments will be offered in support of the contention that only when the information flow reaches the left[1] lateral prefrontal cortex (PFC) does it achieve VR-conscious status.  In the microscopic step, the argument will be made that it is not the left lateral PFC as a whole that mediates VR-consciousness, but rather that VR-consciousness is mediated separately and redundantly by individual pyramidal neurons inhabiting the region.  A divergent/convergent feedforward model of information flow involving a subset of individual neuron within the population will be offered.  Finally, the theory will be extended to account for consciousness of a nonverbally reportable variety.

 

3.  THE MACROSCOPIC STEP:  PLAUSIBILITY OF A FOCAL MODEL

 

     The first objective in developing the single-neuron theory is to determine, on a macroscopic level, just which brain region or regions participate directly in VR-conscious experience.  The commonly held view is that it is the activity of neuronal populations dispersed across multiple cortical and subcortical brain regions that mediates VR-consciousness (Llinas et al. 1998; Damasio 1999, Edelman and Tononi 2000, John 2001).  Importance is usually placed not only on cortical processing but also on thalamocortical loops (Llinas et al. 1998; Edelman and Tononi 2000) and involvement of the upper brainstem and other subcortical structures (Damasio 1999).  With regard to cortical information flow, while there is general acknowledgment of a hierarchical arrangement for posterior cortical subregions (Hubel and Wiesel 1962, 1965; Rockland and Pandya 1979; Maunsell and Essen 1983; Felleman and Van Essen 1991; Barbas and Rempel-Clower 1997; Lamme and Roelfsema 2000; Inui et al. 2004), the flow of information between posterior and frontal cortex is usually regarded as inherently bidirectional and lacking any feedforward directional bias (Fuster 1998).

 

     On the face of it, this multifocal view of conscious information processing seems unavoidable.  It is indeed difficult to imagine how a single brain region could serve as a focus of convergent inputs sufficiently rich to account for the manifest intricacy and multifarious nature of VR-conscious experience.  Additionally, the multifocal view is encouraged by data obtained from brain ablation, neuroimaging, and single-cell electrical recording studies, each of which has demonstrated the importance of widely dispersed brain regions in even the simplest VR-conscious tasks.  Thus, the ablation paradigm, which regards a brain region as important for a given task only if damage to that brain region regularly results in the loss of ability to perform the task, has implicated brainstem, thalamic, limbic, and widespread cortical regions in VR-conscious processing (Crick and Koch 1990a; Damasio 1998; Parvizi and Damasio 2001).  More recently, neuroimaging studies employing functional MRI and related imaging techniques or with those utilizing single-cell recordings from pyramidal neurons in various brain areas have provided direct evidence of a role for widespread cortical and subcortical regions in VR-consciously mediated behavior (Crick and Koch 1990a; Dehaene et al. 1998; Llinas et al. 1998; Raichle 2000).

 

     Despite these arguments, it remains possible that while a distributed model is consistent with the available data, so might be a convergent model.  It may be possible that just a subset or even only one of the areas delineated in the above studies actually subserves VR-conscious experience and that the other areas function only to provide information to this convergent region.  For example, during a VR-conscious visual task, ablation, neuroimaging, and single-cell recording data have together implicated activity spread across primary and secondary occipital cortices, tertiary temporal and parietal association cortices, PFC, and multiple diencephalic and brainstem structures in the context of VR-conscious experience.  Since, however, these disparate regions are highly interconnected, the possibility that only one of the identified regions actually subserves VR-conscious experience and that the other regions serve only to provide input to this focal region cannot be dismissed without further consideration.

 

     For a focal model to gain serious consideration, a brain region would need to be identified that could serve as a locus of convergence for the full set of stimuli that feed VR-conscious experience.  Such a region should manifest certain empirically demonstrable features.  First, the area should be the recipient of afferent connections from brain localities corresponding to each of the sensory, emotional, mnemonic, and other components that make up VR-conscious experience.  Second, it should be the case that the area is adequately connected with brain regions capable of mediating VR-conscious behavioral output.  Third, the area should show activation with neuroimaging or single-cell recording techniques during engagement in VR-consciously mediated tasks.  And fourth, ablation of the area should result in loss of VR-conscious experience.  In the following, the argument will be made that the left lateral PFC (corresponding to portions of Brodmann areas 9, 10, 11, 46, 47) appears to satisfy each of the enumerated requirements.

 

     Afferent Connections:  The first question is whether the left lateral PFC is appropriately positioned to be the recipient of afferent connections pertaining to each of the sensory, emotional, and mnemonic components that comprise VR-conscious experience.  This possibility is often dismissed summarily, but invariably without an accompanying in-depth analysis (eg, see Dehaene 1998).  Yet a review of the neuroanatomical literature suggests that the idea of a convergence zone should not be so casually disregarded.  It has been suggested, for example, that the PFC as a whole functions as a convergence zone, receiving input from most other brain regions (Miller and Cohen 2001; Elston 2003).  Since PFC subregions are strongly interconnected (Barbas and Pandya 1991), any one PFC subregion could be capable of serving as a convergence target for all the other PFC subregions.  In the single-neuron theory, it is the left lateral PFC that is assumed to serve as the final convergence area for the sensory, emotional, and mnemonic components of VR-consciousness, with evidence in support of this contention available for each of these components.

 

     With regard to sensory input, the lateral PFC has long been established to receive input from the occipital, temporal, and parietal association cortices that process the incoming visual, acoustic, and tactile signals that provide the organism with information about its extrapersonal and proprioceptive space (Kuypers et al. 1965; Pandya and Kuypers 1969; Goldman-Rakic and Schwartz 1982;  Petrides and Pandya 1984, 1999; Seltzer and Pandya 1989; Barbas and Pandya 1989; Pandya and Yeterian 1990), including simultaneous convergent input from multiple modalities (Jones and Powell 1970; Chavis and Pandya 1976; Bruce et al. 1981).  Additionally, the lateral PFC receives inputs from the caudal orbitofrontal cortical regions that function as association areas for taste and smell (Baylis et al. 1995, Johnson et al. 2000), with inputs for the latter bypassing the thalamus and projecting solely to the frontal lobes.

 

     With regard to emotion, ablation and activation studies have each supported a role for both anterior cingulate and ventromedial orbitofrontal cortices in assessing the emotional significance of stimuli (Baleydier and Mauguiere 1980, Bush et al. 2000, Phan et al. 2002).  Anatomically, these regions are the recipients of input from the amygdala, the hypothalamus, and the hippocampus (Porrino et al. 1981; Amaral and Price 1984; Goldman-Rakic et al. 1984; Barbas and DE Olmos 1990) which carry the processed visceral stimuli from which emotions are proposed to be comprised (Damasio 1999).  Both regions project strongly to lateral PFC (Eslinger and Damasio 1985) where, it is suggested (Damasio 1999; Gray et al. 2002), emotion and cognition become integrated, with feelings providing “value,” which is needed for decision-making.

 

     With regard to memory, links to lateral PFC are well established for both short-term working memory and long-term distraction-stable memory.  For short-term working memory, findings from ablation, neuroimaging, and single-cell recording studies point to the lateral PFC as a principal site (along with temporal and parietal tertiary association cortex) wherein information is retained for brief periods during the performance of complex problem-solving tasks (Fuster and Alexander 1971; Kubota and Niki 1971; Goldman-Rakic 1992; Cohen et al. 1997; Courtney et al. 1997; D'Esposito et al. 1998; Rainer et al. 1998, 1999; Romo et al. 1999; Fletcher and Henson 2001; Constantinidis and Goldman-Rakic 2001; Constantinidis et al. 2002).

 

     For long-term distraction-stable memory, the lateral PFC plays a key role in the retrieval of episodic memories stored in posterior neocortex and hippocampus (Goldman-Rakic 1992; Fuster 1999), a component of memory that is inherently conscious (Tulving 2002).  Direct connections between the hippocampus and the lateral prefrontal cortex have been established, and ablation (Wheeler 1995) and neuroimaging studies (Fletcher and Henson 2001; Braver et al. 2001, Slotnick et al. 2003) have provided direct evidence of a relationship between lateral PFC and long-term memory function.

 

     Efferent Connections:  On the efferent side, the question is whether the left lateral PFC is able to access the motor programs that presumably mediate VR-conscious behavioral output.  There is ample evidence that this is the case for lateral PFC, which sends outputs to the frontal eye fields, the premotor frontal cortex, the basal ganglia, cerebellum, and superior colliculus (Goldman and Nauta 1976; Alexander et al. 1986, Bates and Goldman-Rakic 1993; Lu et al. 1994; Schmahmann and Pandya 1997).  For VR-consciousness in particular, a critical issue is whether the left lateral PFC is connected adequately with Broca's area, the brain region that provides for syntactical linguistic output.  The pivotal role of Broca's area, situated in the posterior part of the inferior frontal gyrus (Brodmann's areas 44 and 45) of the left frontal lobe, in language expression was established first in 1861 (Broca 1861) and has been amply confirmed in the one and a half centuries that have followed.  For logical, grammatical output, which is the type involved in verbally reporting on VR-conscious experience, the situation is asymmetric, with only the "dominant" hemisphere (the left hemisphere in most humans and almost all right-handers) capable of performing the function.  Left lateral PFC is ideally positioned to influence Broca's area and thereby express verbal reports pertaining to VR-conscious experience since it lies immediately adjacent to it and projects to it strongly (Deacon 1992).

 

     Activation Studies:  The principal evidence for activation of the left lateral PFC during VR-conscious tasks comes from studies of working memory, a cognitive function that is usually regarded as VR-conscious (Baddeley 2003).  As noted above, neuroimaging and single-cell recording studies indicate that lateral PFC is a principal mediating site for working memory.  Additionally, left lateral PFC has been shown directly to become active in neuroimaging studies during tasks involving VR-conscious experience (McIntosh et al. 1999; Kjaer et al. 2001, Stephan et al. 2002).  Event-related potential measurements in humans have shown further that lateral PFC is activated subsequent to posterior cortex in association with performance of VR-conscious tasks, with a mean latency of activity in response to visual stimuli over occipital areas measured at 56 msec and over lateral PFC at 80 msec (Foxe and Simpson 2002).  This temporal sequence of events would be in keeping with a model in which posterior cortex provides preliminary processing of stimuli for subsequent VR-conscious processing by lateral PFC.

 

     Results of Ablation:  If the left lateral PFC were, by itself, the mediator of VR-conscious experience, then ablation of the region should result in its elimination.  Care is needed, however, in applying this syllogism in clinical situations.  To begin with, since the focus up to this point has been specifically on VR-consciousness, not on higher consciousness in general, clinical cases would need to be assessed for the presence or absence of this particular form of consciousness if they were to serve as an appropriate test of the proposed theory as presented so far.  As we will see when we extend the discussion of the theory beyond the confines of consciousness of the verbally reportable variety, it would be damage to the lateral PFC bilaterally that would be expected to eliminate higher consciousness in general.

 

     There is also a methodological concern having to do with the extent of the cortical damage incurred in reported cases.  As will be evident when the theory is described on the microscopic level, only nearly complete inactivation of the left lateral PFC would be predicted to eliminate VR-consciousness, and only complete inactivation of the lateral PFC bilaterally would be predicted to eliminate higher consciousness more generally.  Thus, we note that some authors (Bogen 1995; Alexander and Stuss 2000; Taylor 2001) have cited clinical examples in the literature, such as the celebrated case of Phineas Gage (Damasio 1999), where extensive prefrontal lesions failed to eliminate higher conscious experience, and have used these to argue that the lateral PFC by itself cannot be the sole mediator of higher consciousness.  As pointed out by Crick and Koch (1998), however, in none of these cases did the damage include the entirety of the lateral PFC, rendering their significance uncertain.  With only partial removal of the relevant cortex, the remaining tissue might have been able to maintain residual higher conscious function.  On the other hand, situations in which the lateral PFC is known to have been entirely destroyed bilaterally, such as in patients with advanced Pick's disease, massive bilateral strokes, extensive anoxic damage, or massive surgical bilateral frontal lobe ablations as reported by Walter Dandy (1946), the clinical picture is of a persistent vegetative state in which the victim is devoid of any higher level consciousness (Sato et al. 1989; Laureys et al. 1999; Heilman and Valenstein 1993; Laureys 2004).  And in cases in which the left lateral PFC is known for certain to have been destroyed entirely, such as in global aphasia from a massive left middle cerebral artery stroke, VR-consciousness is clearly lost, with the right hemisphere providing residual, nonverbal higher conscious function (Heilman and Valenstein 1993).  While such cases are consistent with the notion that VR-consciousness is mediated solely by the left lateral PFC, and that higher consciousness in general is mediated solely by the lateral PFC bilaterally, the extensive nature of the damage in these reports ruins the specificity of the correlation.

 

      Cases are needed in which the left lateral PFC (or the lateral PFC bilaterally) has been completely removed or rendered inoperative while other structures have been left intact, and an assessment made specifically for the presence or absence of VR-consciousness (or higher consciousness more generally).  Such cases have yet to be reported.

 

     In summary, anatomical and functional studies have been reviewed that point to the importance of left lateral PFC for VR-conscious experience.  Other authors have also emphasized the importance of the lateral PFC to consciousness (Crick and Koch 1998; Rees et al. 2002; Stephan et al. 2002).  The present theory goes further, however, in proposing that the left lateral PFC, situated within a divergent/convergent feedforward information processing system, serves by itself as the direct neural correlate for VR-consciousness.  On the other hand, suggestions have occasionally been made favoring other brain regions as a center for consciousness (Dandy 1946; Taylor 2001).  The parietal tertiary association cortex is an attractive possibility (Taylor 2001), but the parietal lobes do not receive direct information pertaining to smell, which is a common component of VR-conscious experience.  Alternatively, the orbitofrontal cortex, which is a region that receives convergent input from all modalities, might be considered to be a candidate for a localized NCC.  However, complete orbitofrontal ablations, while they impair emotional processing, do not eliminate VR-consciousness (Eslinger and Damasio 1985; Heilman and Valenstein 1993; Damasio 1999).   

 

     The localization to the left lateral PFC proposed in this paper, it should be noted, may not be the full extent to which VR-consciousness is localized.  The lateral PFC might itself be further divisible.  In fact, support has been garnered for the lateral PFC encompassing two functionally distinct subregions, one dorsolateral (portions of Brodmann 9, 46) the other ventrolateral (portions of Brodmann 10, 11, 47), the two subregions differing with respect to both anatomical connections and type of information being processed (Oliveri et al. 2001).  It has been suggested that the established division of the posterior cortex into "dorsal" and "ventral" streams (Ungerleider and Mishkin 1982; Goodale and Milner 1992; Milner and Goodale 1993; Foxe and Simpson 2002) continues into the frontal lobes, with the dorsolateral PFC receiving input from the dorsal stream via the parietal association cortex and the ventrolateral PFC receiving input from the ventral stream via the temporal association cortex (Milner and Goodale 1995).  Since both areas are strongly connected to Broca's area, both are in a position to affect verbal output.  Further, since both areas are strongly interconnected, either could ultimately be the sole mediator of VR-conscious experience with the one serving to provide input to or receive output from the other.  In fact, Milner and Goodale (1995) have argued for just this possibility, suggesting that only the ventral stream is associated with VR-conscious experience and that the dorsal stream processes information VR-unconsciously.

 

     In the discussion that follows, the question of which subregion by itself might mediate VR-conscious experience will be left undecided and the generic term "lateral PFC" will be used to refer to the area in general.  In any case, whatever the final localization of the macroscopic anatomical substrate for VR-consciousness might be, all that is needed for the further development of the single-neuron theory is that a macroscopically focal correlate for VR-consciousness exists that is relatively homogeneous and is in a position to receive all the sensory, emotional, and mnemonic stimuli that characterize the VR-conscious experience.

 

4. THE MICROSCOPIC STEP:  THE SINGLE-NEURON THEORY

 

     Subsequent to the question of which macroscopic brain region mediates VR-consciousness is the question of how the neurons populating this region accomplish the task.  According to the generally accepted view, the participating neurons join their activity into a single VR-conscious experience via dynamic interactive mechanisms.  The single-neuron theory posits instead that a subpopulation of neurons in the left lateral PFC, rather than joining their activity into a single VR-consciousness, remain separately VR-conscious and produce the illusion of a joint VR-consciousness by virtue of the joint action of their outputs.  In the model, each of the VR-conscious neurons at any moment is proposed to experience not merely a fragment of a higher-order joint VR-consciousness but, rather, the whole range and complexity of VR-conscious experience generally attributed to the population as a whole.  Plausible candidates for the role of VR-conscious neurons are the layer 5 pyramidal cells of the left lateral PFC.  A pivotal role for these neurons in conscious processing has been suggested by other investigators (Crick 1994; Orpwood 1994; Gilbert 1998; Bieberich 2002).  It will be useful to briefly review the main characteristics of these neurons before describing how they might individually serve as neural correlates of VR-consciousness.

 

Anatomical and Functional Characteristics of Lateral PFC Layer 5 Pyramidal Neurons

 

     Like all neurons, layer 5 pyramidal cells function by integrating the electrical responses of an enormous array of dendritic synaptic inputs, the result of which determines a single channel of axonal output consisting of sequences of action potentials of identical shape and amplitude.  Early on, the dendritic tree was modeled as a single entry point (McCullough and Pitts 1943), with inputs simply summing linearly to produce axonal output.  In the late 1950s, Rall (1959, 1967) introduced the passive cable model, in which the spatial extent of the dendritic tree was taken into account and accommodation was made for the effects of location and amplitude of postsynaptic potentials on the neuron's axonal response.  The passive cable model provided for a degree of intradendritic information processing that was not possible with the earlier "point neuron" model.  In recent years, the presence of significant nonlinear intersynaptic effects occurring on multiple levels has been discovered (Cash and Yuste 1999; Magee 1999; Koch 1999; Larkum et al. 1999a; Magee and Cook 2000; Spruston 2000; Hausser et al. 2001; Wei et al. 2001), suggesting that considerable intradendritic computational processing precedes the eventual generation of axonal action potential.

 

     Additionally, there is evidence that intradendritic processing occurs independently in separated compartments before an overall effect is delivered to the soma (Koch 1997).  For layer 5 pyramidal neurons, a unique anatomy facilitates this compartmentalization.  Most notable is the long ascending dendritic shaft, extending 1.3 mm from the cell body in layer 5 to the apical tip in layer 1, that separates the proximal basal dendrites from the distal apical dendrites.  Midway along the shaft are the oblique dendrites, both proximal and distal.  Recent evidence suggests that the output of the various compartments interact via two intraneuronal dendritic action potentials.  The first is a forward propagating, calcium-mediated potential originating at the distal end of the dendritic stalk that is produced as a result of nonlinear integration of apical dendritic inputs.  This action potential courses from the apical end of the dendritic shaft to the soma where it induces a burst of axonal action potentials (Helmchen et al. 1999; Schiller et al. 1995, 1997; Schwindt and Crill 1999; Larkum et al. 1999a; Williams and Stuart 2002).  The second is a backpropagating sodium-mediated action potential that is triggered by axonal firing and that courses from the soma back into each of the dendritic arbors (Stuart and Sakmann 1994).  The two action potentials appear to be coupled, with the backpropagating potential lowering the threshold for triggering of the forward propagating potential, provided the two potentials occur within a few milliseconds of each other (Larkum et al.1999a, 1999b; Larkum et al. 2001; Schaefer et al. 2003; Larkum et al. 2004).  The coupling has been shown to depend crucially on the morphology of the participating dendritic branches (Mainen and Sejnowski 1996; Schaefer et al. 2003; Vetter et al. 2001).

 

     Aside from the nonlinear interactions present at the intercompartmental level, nonlinear interactions have been found within the dendritic arbors as well (Cash and Yuste 1999; Magee 1999; Koch 1999; Larkum et al. 1999a; Magee and Cook 2000; Spruston 2000; Hausser et al. 2001; Wei et al. 2001; see Magee 2000 for a review).  It has been suggested that nonlinear interactions between neighboring synapses provide for increased computational capability in dendritic trees (Segev and London 2000; Van Ooyen et al. 2002; see Hausser and Mel 2000 for a review).

 

     Because of the anatomy and compartmentalization of the layer 5 pyramidal neurons, they are ideally positioned to integrate input from the different cortical layers (Spratling 2002; Thomsen et al. 2002a; Briggs and Callaway 2005).  Thus, the basal dendrites, which are situated near the cell body in layer 5, receive intralaminar input from other pyramidal neurons in the same layer (Lubke et al. 1996; Thomson et al. 2002b; Douglas and Martin 2004).  Higher up, the oblique dendrites receive inputs from layers 2, 3 and 4 (Schaefer et al. 2003).  Still more distally, the apical dendrites receive inputs from layers 1, 2 and 3 (Schaefer et al. 2003).

 

     A functional interpretation of these inputs is provided by a proposed microscopic feedforward circuit that includes the layer 5 pyramidal neurons (Gilbert and Wiesel 1986; Thomson et al. 2002a,b; Thomson and Bannister 2003).  The main pathway in this circuit is characterized by a feedforward flow beginning with the layer 4 stellate (granular) neurons, which receive incoming feedforward signals from other cortical and thalamic regions, and moving on to the pyramidal neurons in layers 2 and 3, then on to layer 5 pyramidal neurons, and finally out to pyramidal neurons in layers 1 and 5 elsewhere in the prefrontal cortex, as well as to premotor cortex, posterior cortex, thalamus, basal ganglia, and superior colliculus.  Return input from these distant targets arrive via diffusely projecting layer 1 fibers, whose signals are received by the pyramidal neurons in layers 2 and 3, and 5 (the latter receiving inputs from layer 1 both directly and also indirectly via inputs from layers 2 and 3).  In this scheme, the input from layer 1 consists of diffusely encoded signals derived from thalamus, hippocampus, and posterior and frontal limbic areas (Barbas and Rempel-Clower 1997; Rempel-Clower and Barbas 2000; Mitchell and Cauller 2001; Barbas and Hilgetag 2002).  These inputs have been postulated to be associated with attention, feedback, and context (Williams and Stuart 2002; Douglas and Martin 2004)).  The inputs to layer 2 and 3 pyramidal neurons derive from both local and distant layer 2 and 3 pyramidal neurons located throughout the lateral PFC.  These layer 2 and 3 pyramidal neurons are, especially in the lateral PFC, highly interconnected and have been proposed to serve an important role in the maintenance of reverberatory circuits underlying working memory (Kritzer and Goldman-Rakic 1995; Gonzalez-Burgos et al. 2000; Melchitzky et al. 2001).

 

     An important implication of these pathways and connections (see Larkum 2001), and one that will be used in developing the single-neuron model, is that the apical dendrites of layer 5 pyramidal neurons, by virtue of being recipients of layer 2/3 pyramidal cell output, are in a position to receive inputs representing the full range of VR-conscious content.  In the following sections, we will build on this and the other characteristics of layer 5 pyramidal neurons to present a specific way in which the single-neuron theory of consciousness might play out at the microscopic level.  It should be noted that it is not contended that the model to be presented is the only way a single-neuron theory can be instantiated.  For example, it may be that neurons other than the layer 5 pyramidals might equally well serve as loci for VR-consciousness, or it may turn out that the details of layer 5 pyramidal function as described above need modification.  The purpose of the presentation is mainly to provide a specific instantiation of the theory in order to demonstrate that a single-neuron model is in principle feasible.  The model will be built upon a framework of convergent/divergent feedforward information flow, in which feedback and reentrant circuits play only an adjunctive role.  The features of the model will be presented in a feedforward sequence:  A) first, a mechanism of information encoding at the neuronal population level will be described for the network of neurons that will serve as the afferent input for VR-conscious neurons; B) second, the process by which signals converge upon the dendritic trees of VR-conscious neurons and induce intradendritic NCCs will be considered; C) third, a mechanism by which the dendritic NCCs reexpress themselves at the neuronal population level will be suggested.

 

A) Population Encoding of the Information to be Inputted to the VR-Conscious Neurons:

 

     We begin by examining the information processing characteristics of layer 2 and 3 pyramidal neurons in left lateral PFC, which will be presumed to be the source of the inputs to layer 5 pyramidal neurons that lead to VR-consciousness.  In overview, it is hypothesized that the convergent input to the left PFC as a whole, argued in the previous section to contain the sensory, emotional, and mnemonic signals that feed VR-consciousness, passes from layer 4 stellate neurons to layer 2/3 pyramidal neurons, and that the relevant information is encoded in the spatial arrangement of a synchronously firing subpopulation of these layer 2/3 neurons.  We now consider this process in more detail.

 

     As a preliminary, we need first to address the nature of the neural code.  There has been an ongoing debate over how information is encoded by neuronal populations in neocortex.  The principal questions have to do with the relative importance of individual neuronal spikes in cortical processing and whether spikes derived from different neurons are bound by means of temporal synchrony.  The early view was that information is encoded in the cortex by the average rate of axonal action potential production over a given time interval, typically between 20 and 200 msec, and that the exact timing of individual spikes is unimportant. (Adrian 1926; Barlow 1972; Shadlen and Newsome 1994; Shadlen and Newsome 1998; Shadlen and Movshon 1999; Yazdanbakhsh et al. 2002; Rolls et al. 2003).  In recent years, however, the alternative possibility, that each spike individually carries relevant information, has gained a growing following (Softky and Koch 1993; Gray 1999; Borst and Theunissen 1999; Williams and Stuart 2000; Abeles 2004).  A related idea, that brain neuronal activity can be synchronized, and that synchrony is in fact a widely occurring and robust phenomenon that might serve to define functional relationships between spatially distributed cortical neurons, has also gained considerable support (Abeles 1991; Konig et al. 1995; Singer and Gray 1995; Lumer et al. 1997; Stevens and Zador 1998; Lachaux JP et al. 1999; Usrey and Reid 1999; Grammont and Riehle 2003).  The possible mechanisms underlying the induction of temporal synchrony of cortical neuronal activity have been explored (Niebur et al. 2002; Reyes 2003; Segev 2003; Nowotny and Huerta 2003), as has its possible behavioral relevance (Sougne and French 2001).  In particular, it has been suggested that synchronous firing promotes synergy between spikes arriving simultaneously at target neurons, thereby providing a powerful signal selection mechanism (Niebur et al. 2002; Averbeck and Lee 2004).

 

     For the theory presented in this paper, it will be assumed that individual spikes do matter, and that synchronous firing identifies subpopulations of neurons that encode salient information in their spatial arrangement.  The model employed is an adaptation of the well-studied "synfire chain" mode of cortical signal transmission (Abeles 1991, 2004; Aviel et al. 2002), which invokes the importance of both individual spike timing and the synchronization of spatially dispersed neuronal activity.  The synfire chain theory describes a process of synchronous convergent/divergent feedforward signal transmission through multiple neuronal layers.  Synchrony is hypothesized to result from an arrangement in which, for each layer, there is a pool of neurons with the property that each neuron in the pool:  a) receives afferent inputs from all the neurons in the pool in the previous layer, and b) projects its output to all the neurons in the pool in the next layer.  Empirical observations supporting the presence of synfire chain activity in the brain have been reported by a number of investigators (Abeles 1991; Miller 1996; Prut et al. 1998; Abeles 2004; Ikegaya et al. 2004) and their properties have been analyzed through theoretical simulations (Abeles 1991; MacGregor et al. 1995; Aertsen et al. 1996; Hertz and Prugel-Bennett 1996; Diesmann et al. 1999; Arnoldi et al. 1999; Gewaltig et al. 2001; Sougne and French 2001; Yazdanbakhsh et al. 2002).  Theoretical simulations have indicated how, in a multilayered feedforward neural network consisting of integrate-and-fire neurons, synchronous firing on a millisecond time scale arises in successive layers, even if the initial input to the chain are asynchronous (Pinsky 1995; Arnoldi and Brauer 1996; Hertz and Prugel-Bennett 1996; Marsalek et al. 1997; Rudd and Brown 1997; Campbell et al. 1999; Neltner et al. 2000; Niebur et al. 2002; Reyes 2003; Segev 2003; Nowotny and Huerta 2003).  The synchronous chains have been shown to be stable against noise, provided a sufficient number of neurons populate each pool, estimated to be on the order of 100 neurons (Abeles 1991; Postma et al. 1996; Diesmann et al. 1999; Gewaltig et al. 2001; 2001).  In the stable state, essentially all response spikes in a volley fall within ±1 ms, which is within the precision of experimentally observed neuronal firing patterns, despite the presence of a membrane time constant of 10 ms or more (Diesmann et al. 1999; Gewaltig et al. 2001).

 

     In the proposed single-neuron theory, the presence of a synfire chain comprising two consecutive pools of neurons is hypothesized:  one pool in lateral PFC layer 2/3, the other pool in lateral PFC layer 5.  The pool in layer 2/3 is taken to comprise a subpopulation of synchronously firing pyramidal neurons that have received feedforward input from layer 4 pertaining to VR-consciousness, with that information being encoded in the spatial arrangement of the participating neurons.  The presence of identifiable spatial patterns of neuronal excitation in cortical neuronal populations has been reported in a variety of contexts.  These include the peculiar complex logarithmic relationship between spatial patterns of excitation in the retina and those in area V1 pyramidal neurons (Schwartz 1980; Alexander et al. 2004), the specific topographic pattern of layer 5 pyramidal neurons in cat visual cortex that exhibited repeating temporal spike sequences (Ikegaya et al 2004), the specific spatial patterns of neuronal response to air currents in the cricket cercal system (Jacobs and Theunissen 2000), and the work of Grossberg et al. (1999), which implicates an important role for the topography of primate visual cortex in shaping neuronal responses at subsequent levels of cortical visual processing.  In the single-neuron theory, it is the topography of neuronal activation in the synchronously firing layer 2/3 pyramidal population that is assumed to carry the information to be delivered to the layer 5 pyramidal neurons.

 

B) Conversion of Afferent Population Patterns into Intradendritic Neuronal Electrical Patterns:

 

     The next issue in the feedforward progression is the matter of how the afferent signals from the synchronously firing layer 2/3 pyramidal neurons converge upon layer 5 pyramidal neurons and lead to intradendritic electrical activity that serves as the neural correlate for VR-consciousness.  Consideration will be given first to the synaptic activation patterns induced in the layer 5 pyramidal neurons, and then to the conversion of these synaptic activation patterns into spatial electrical patterns within the dendrites.  For the model to work, two hypotheses are needed:

 

HYPOTHESIS 1: THE MAPPING FROM POPULATION TO DENDRITIC IS HOMOTOPIC AND INFORMATION PRESERVING:  Despite its vital importance to an understanding of neuronal information processing, there is little available data pertaining to the neurons of origin for particular synaptic inputs within a dendritic tree.  That is, while neurons of origin have been identified with respect to one or another overall dendritic compartment, similar information with respect to individual synaptic inputs within a compartment are not yet available.  In the absence of such empirical clues, I would suggest that a plausible possibility is that the topographic mapping of inputs from the synchronously firing layer 2/3 pyramidal neurons onto layer 5 pyramidal dendrites is homotopic (the shape of the one can be continuously transformed into the shape of the other) and information preserving.  Homotopic, information preserving mappings have been found elsewhere in the nervous system, such as in the complex logarithmic mapping of retina onto V1 already mentioned (Schwartz 1980; Alexander et al. 2004), and in the homotopic connections between the cerebral hemispheres via the corpus callosum (Mitchell and Macklis 2005).  The suggestion is that the axons arriving from the synchronously firing layer 2/3 pyramidals retain their relative positions and preserve topological information in coursing toward their targets and do not "criss-cross" on their way to innervating the dendritic tree of the layer 5 pyramidals.  Whether this is in fact the case will need to be determined experimentally.  Since it plays a key role in the hypothesized theory, testing for its presence will serve as an empirical test for the plausibility of the proposed theory.

 

HYPOTHESIS 2: SIMILAR PATTERNS ARE RECEIVED BY MULTIPLE NEURONS:  A second assumption is that the projections from layers 2 and 3 to layer 5 involve convergence of a similar pattern onto multiple neurons.  If the topographic pattern of synaptic excitation is relevant in determining whether it induces axonal firing or not, then this is in effect the central assumption of the synfire chain theory that was discussed in detail above, and upon which the currently proposed model is based.

 

     With these hypotheses in hand, a mechanism can be described by which the spatially encoded pattern at the population level in layer 2/3 reappears as a spatially encoded pattern of intradendritic electrical activity in layer 5 pyramidal cells, this pattern serving as the neural correlate for VR-consciousness.  We start by proposing that the spatial electrical pattern corresponding to VR-consciousness forms within the thin distal branches of the apical dendritic tree.  While alternative locations, such as the thin oblique dendritic branches, could also be chosen, the apical branches are selected here to provide a concrete example of how the overall model would work.  By hypothesis 1, it is assumed that a specific pattern of synaptic excitation is induced by convergent input from a synchronously firing pool of layer 2/3 pyramidal neurons, and that this pattern of synaptic excitation is a homotopic, information preserving transform of the spatial pattern of the layer 2/3 neuronal pool.  By hypothesis 2, it is assumed that a similar pattern of input is received by a pool of layer 5 pyramidal cells.

 

     The shape of the electrical response within the dendritic branches of each neuron receiving this pattern of synaptic input will, in turn, be an ordered transformation of the shape of the incoming signals (Bieberich 2002, Orpwood 1994, and Livingstone 1998), modified by the effects of synapse efficiency and local nonlinear effects (including those due to the state of depolarization of the synapses at the time of signal input, the interactive effects between synapses due to induced local chemical and electrical changes, and the morphology of the dendritic tree itself), each of which is likely somewhat different for each of the neurons in the receiving pool (Spruston et al. 1995; Mainen and Sejnowski 1996; Hoffman et al. 1997; Agmon-Snir et al. 1998; Cash and Yuste 1999; Magee 1999; Koch 1999; Larkum et al. 1999a; Magee and Cook 2000; Spruston 2000; Magee 2000; Vetter et al. 2001; Hausser et al. 2001; Wei et al. 2001;; Schaefer et al. 2003; Polsky et al. 2004).  The variations in the shape of the intradendritic electrical responses across the different neurons would be assumed to be relatively small, so that variations in VR-conscious experiences of the different neurons would be relatively small as well.  I will consider later the potential impact that large variations might have on the single-neuron theory.

 

     Of critical importance to the singe-neuron theory is the assumption that the intradendritic electrical pattern that forms in the distal apical branches, which is proposed to serve as the direct neural correlate for VR-consciousness, extends through only a portion of the neuronal dendritic tree.  It is hypothesized that its ultimate effect on axonal firing therefore comes only after it combines nonlinearly with other intradendritic electrical activity, both within the more proximal apical tuft in generating a calcium-mediated dendritic action potential and in the subsequent interaction between the different dendritic compartments.  That is, the local electrical activity that serves as the neural correlate for VR-consciousness in the distal apical tree of a given neuron is assumed to affect neuronal output only in the context of other inputs received by the neuron at the same time.  Again, this effect is presumed to be different for the different layer 5 pyramidal neurons in the pool, and therefore the responses of the different neurons to the same (more or less) VR-conscious electrical activity forming in the distal apical dendrites will be different.  In the next section, this fact will be used in proposing a mechanism by which the intradendritic VR-conscious shape reexpresses itself at the neuronal population level.  Before turning to that mechanism, it is important to consider whether the electrical activity purported to serve as the neural correlate for VR-consciousness has the right type and degree of complexity to encode for conscious experience.

 

COMPLEXITY OF INPUTS TO THE LAYER 5 PYRAMIDAL NEURONS:   For the single-neuron theory to be plausible, it needs to be the case that the electrical activity in the distal apical branches of an individual neuron is by itself complex enough to support the complexity of VR-conscious experience.  The relevant observation pertaining to this requirement is that cortical pyramidal neurons receive a huge number of afferent signals over their dendritic trees.  It is estimated that for humans each cortical pyramidal neuron incorporates between 20,000 and 40,000 synapses within its dendritic tree (Abeles 1991), a number larger than is usually appreciated and one that allows for an impressive complexity of information encoding.  For example, if only one tenth of one percent of the synapses of a cortical pyramidal neuron were assumed to participate in a given VR-conscious experience, and if only a simple binary code were being used (that is, one in which the synapse is either active or inactive), then 240 or about one trillion different patterns could be encoded by that neuron.

 

     Alternatively, as noted by Bieberich (2002), if it is assumed that 5,000 simultaneously arriving synaptic input bytes, corresponding to 5,000 active spines in the dendritic tree, are turned over with a frequency of 50 Hz, then a single neuron would be capable of processing 5 mB of information per second.  This, Bieberich argues, constitutes a rate of information processing adequate to plausibly account for the complexity of VR-conscious experience.

 

     By either of these calculations, individual cortical pyramidal neurons appear capable of processing information of a complexity greater than often appreciated, and quite possibly sufficient to mediate the entirety of the VR-conscious experience.

 

CONVERGENCE OF MODALITIES UPON INDIVIDUAL LAYER 5 NEURONS:  Another requirement that the single-neuron theory must meet is that the dendritic trees of the VR-conscious neurons would need to individually be recipients of all of the kinds of inputs that make up the VR-conscious experience.  If it is assumed that the lateral PFC as a whole receives all of the inputs that comprise VR-conscious experience, then the question is whether it plausible that there are individual pyramidal neurons within the region that do likewise?  That is, is it plausible that the information that converges upon the region as a whole converges separately on each of a subgroup of pyramidal neurons populating the region?  In support of the presence of such an arrangement is the clinical observation that focal lesions within lateral PFC do not result in modality-specific deficits but only diminish the region's function in a general manner.  This suggests at least some degree of microscopically redundant convergence throughout the extent of the region.  Additionally, individual cortical neurons typically receive inputs from thousands of other neurons (Koch 1997, Schuz 1998) and so are in a position to serve as nodes of converging information.  Aside form general considerations, however, are the reports that direct single-cell recordings have identified neurons in lateral PFC that respond selectively to conjoint visual and auditory stimuli (Aou et al. 1983), to conjoint visual, auditory and tactile stimuli (Tanila et al. 1992), and to conjoint object and location features (Rao et al. 1997). Whether a subgroup of lateral prefrontal neurons exists in which each neuron in the subgroup receives convergent input from all the sensory, emotional, and mnemonic stimuli that comprise VR-conscious experience is yet to be empirically determined.

 

C) Generation of the Outgoing Signal:

 

     Supposing that the spatial pattern of distal apical intradendritic electrical activity of left PFC layer 5 pyramidal neurons serves as the neural correlate for VR-conscious experience, there is then the vexing question of how the information encoded at the dendritic level is passed on to brain output mechanisms that control behavior.  In particular, how can dendritic signal patterns of high complexity converge into a single axonal output channel without a drastic loss of information?  On the face of it, it would appear that such information loss is unavoidable, since axons transmit action potentials that are uniform in shape and amplitude and therefore only the timing of the transmitted spikes can be used to encode information (Adrian 1926).  Across a range of settings, the information contributed by individual axonal spikes has been measured to be on the order of one bit to 3 bits per spike, with an axonal information transmission rate therefore reaching no more than several hundred bits per second (Bialek et al. 1991, Borst and Theunissen 1999).  This rate of information processing is far smaller than that performed by the dendritic tree.  How, then, can a gross loss of information be averted?

 

     A possible solution to this problem may reside in another application of synchronous neuronal firing, one that is inspired by a model of neuronal information processing recently proposed by Bieberich (2002).  The idea is that a mechanism by which the VR-conscious electrical activity present within the layer 5 pyramidal dendrites might be reexpressible at the population level in the form of the spatial arrangement of a synchronously active subset of the layer 5 pyramidal cells.  Conceptually, the sequence is as follows:

 

   1) First, as described above, similar VR-conscious spatial patterns of electrical activity are formed in the distal apical trees of a population of synchronously activated layer 5 pyramidal neurons in the left lateral PFC.

 

   2) Next, the VR-conscious spatial patterns interact nonlinearly, at both the intracompartmental and intercompartmental levels, with the other inputs received by these layer 5 neurons.

 

   3) The results of these interactions are assumed to differ across neurons in the population and therefore to have different effects on the propensity for axonal spike generation in the different neurons in the population, such that some neurons fire while others remain silent.

 

   4) As a consequence of (3), only a subgroup of neurons in the population respond with synchronous firing, with the active subgroup marking out a spatial pattern at the population level.  The spatial pattern, it will be noted, would in part have been dictated by the spatial pattern of the VR-conscious dendritic electrical activity in the neurons of the population.

 

   5) If there are N neurons in the starting population and only M neurons fire (M<N), then the number of different subgroups that can be formed (and therefore the number of different spatial arrangements of firing neurons that can arise) is given by the combination of N things taken M at a time: N!/M!(N-M)!  For even small populations, this is a huge number.  For example, say there are 100 layer 5 pyramidal neurons that receive synchronous dendritic inputs of similar shape from the layer 2/3 neurons, and that half of these 100 neurons fire.  Then the number of different spatial patterns that can form would be equal to the combination of 100 things taken 50 at a time, or (100!)/(50!)(2) = 4 x 1012, an enormous number that matches the complexity of dendritic input as calculated above.

 

   6) The spatial pattern formed by the subpopulation of synchronously firing left lateral PFC layer 5 pyramidal neurons, which incorporates the interaction between VR-conscious information and other information received by the layer 5 pyramidal neurons, serves as the source of conscious output from the left lateral PFC to other brain regions.

 

     While this mechanism may provide a means for the information contained in the intradendritic VR-conscious electrical patterns to be re-expressed at the population level, there is the additional question of whether enough neurons are present in the proposed synchronous populations in layers 2/3 and 5 to affect gross behavior.  As so far described, this would appear unlikely, since previous work with synfire chains has usually associated the phenomenon with relatively small neuronal groups (Abeles 1991).  A realistic model would need to incorporate the possible presence of many small competing synchronous neuronal groups.  For a single dominant synchronous group to arise, still additional assumptions would have to be made.  This issue is, of course, not unique to the theory presented here.  It is a challenge that must be faced by most current neuronal network models of consciousness.

 

     A plausible approach to solving this problem may be to invoke the concept of attention as a mechanism for amplifying the effects of selected synchronously firing neuronal pools.  Studies have, in fact, been reported that suggest a role for attention in increasing the synchrony of neuronal firing (Steinmetz 2000; Fries et al. 2001), possibly mediated by gamma frequency oscillations (Crick and Koch 1990a,b; Niebur et al. 1993; Niebur and Koch 1994).  In this way, attention might serve as an "on-off" switch for lateral PFC conscious function.  The detailed nature of the attentional mechanism by which the small population of synchronously firing neurons might gain control over VR-conscious output systems can only be speculated on at present.  One possibility is that the "bursty" output of the layer 5 pyramidal neurons (Crick 1994; Schiller et al. 1995, 1997; Helmchen et al. 1999; Schwindt and Crill 1999; Larkum et al. 1999a; Williams and Stuart 2002), in association with cortico-thalamo-cortical loops (Llinas et al. 1998), may provide for the proliferation of the synchronous groups.  This could conceivably be accomplished through competition, as in the "neural darwinism" mechanism described by Edelman (1993), or perhaps via merging of separate synfire chains (Arnoldi et al. 1999; Hayon et al. 2005).  Yet another possibility might involve the creation of a fractal architecture in which multiple copies of the synfire neuronal pool arise as a result of the fractal geometry of axonal branching (Bieberich 2002).  With each of these proposed mechanisms, the lateral PFC would be envisioned as functioning in a “holographic” manner, with the same information represented repeatedly in neurons dispersed across the full extent of the region.  As a result, it would be expected that damage to the entire lateral PFC would be required to eliminate the corresponding conscious functioning, a point that was stressed earlier in Section 3.

 

     In summary, a case has been made for the possibility that, at the microscopic level, VR-consciousness is mediated by single neurons rather than by a neuronal population.  The macroscopic step was necessary to provide for individual neurons that receive all the inputs comprising VR-conscious experience and for the supposition that there might be groups of such neurons that individually receive similar input.  In the microscopic step, it was argued additionally that individual neurons are by themselves sufficiently complex to account for the complexity of VR-conscious experience and that a mechanism may be present that translates the spatial pattern of dendritic activation within synchronously firing neurons into the spatial arrangement formed by those neurons at the population level.

 

5. ADDITIONAL CONSIDERATIONS:

 

     Other Conscious Regions:  The discussion so far has been limited to the case of VR-consciousness, that form of consciousness largely responsible for the debate over the "hard" problem of consciousness.  It was useful to invoke this restriction in order to narrow the focus of the presentation.  The theory can, however, be extended to include forms of consciousness that are not verbally reportable.  Indeed, patients with lesions that result in the loss of VR-consciousness often appear to manifest residual nonverbal consciousness, presumably mediated by structures outside the left lateral PFC.  Damasio (1998, 1999) has suggested that the residual consciousness observed in such patients be regarded as one of two overlapping types of consciousness, a non-verbal "core consciousness" mediated by midline cortical and subcortical structures, and a linguistically competent "extended consciousness" mediated by lateral neocortical structures.  The notion that a single brain might contain multiple centers of consciousness has been suggested by other investigators as well (Geschwind 1981, Edelman and Tononi 2000).

 

     A particularly intriguing possible center of nonverbal consciousness is the right lateral PFC. Since this region is anatomically homologous to the left lateral PFC, being anatomically distinguishable from the latter principally by its lack of direct connections with Broca's area and related structures, it might be possible that it mediates a nonverbal form of higher consciousness separate from that mediated by the left hemisphere.  In 1973, Rolando Puccetti suggested exactly this possibility (Puccetti 1973), proposing that the apparent duplication of consciousness that results from corpus callosum resection (the so-called "split-brain" procedure) is best explained by assuming that the right and left hemispheres maintain separate conscious streams not only after callosal disconnection but before the disconnection as well.  According to his hypothesis, the callosal section merely serves to uncouple what are already, in the normal state, two anatomically separate consciousnesses.  Such a notion would fit well with the single-neuron theory, which readily admits to the existence of multiple anatomically separate centers of conscious experience.

 

     In Puccetti's model, it should be noted, the hypothesis is that there are no more than two conscious centers present, and the discussion is limited to the macroscopic level only.  With the presently proposed theory, an additional step is taken, that of proposing that many macroscopic conscious centers may be present in a single brain, and that the multiplicity of consciousnesses extends to the microscopic single-neuron level as well.  The overall picture would be one in which multiple macroscopic centers of consciousness are proposed to exist, including ones located throughout neocortical, subcortical, and brainstem regions, with each center composed, in turn, of populations of neurons that individually and redundantly mediate the conscious activity appropriate to the brain region within which they reside.

 

     Relationship between the Single-Neuron Theory and Other Anatomical Theories of

Consciousness:  The theory presented in this paper is compatible with many of the anatomical and functional features that characterize other recently proposed theories of consciousness.  For example, Dehaene (1998) places the lateral PFC in a prominent position in his description of Baars' global workspace theory (Baars 1988; Baars and Franklin 2003), Llinas (1998) focuses on thalamo-cortico-thalamic oscillating loops, Damasio (1998) distinguishes between a brainstem mediated core consciousness and a cortically mediated extended consciousness, Dandy (1946) favors a striatal dominance in his theory of consciousness, and Edelman (1993) proposes a theory based on “neural darwinism.”  The neuroanatomical and functional proposals made in these theories are for the most part compatible with the single-neuron theory.  The principal differences are that with the single-neuron theory, the lateral PFC by itself is assumed to serve as the macroscopic correlate for higher consciousness, with the other structures playing only an adjunctive role, and the individual neuron is assumed to serve as the microscopic correlate for consciousness of all kinds, while in the other models, consciousness is always a network property.

 

     Philosophical Caveat:  The legitimacy of employing "experience" as a target of scientific investigation is not, it should be noted, universally accepted.  It has, for example, been argued by Dennett (1991) and others that "subjective experience" as illusory and that once all the brain processes associated with a given behavior are objectively described there is nothing left to be explained.  Dennett's arguments, if correct, would eliminate the need to localize a mind/brain interface or to solve a "binding problem."   If, on the other hand, subjective experience is a phenomenon in need of explanation, then the proposed single-neuron theory, which shifts the locus of the mind/brain interface from a macroscopic to a microscopic domain, might provide fresh avenues for addressing Chalmers' "hard problem."  In the section of the paper entitled “Speculative Implications” I will provide a specific example of how this might play out.

 

    Other Single-Neuron Models: Comparison of the single-neuron theory with other models that focus on single neurons may help clarify just what the single-neuron theory does and does not propose.  For example, it is important to distinguish the function of the single neurons in the single-neuron theory from those referred to as "grandmother cells," single neurons whose activation corresponds to the experience of a single memory (Thorpe 1998).  The grandmother cell concept has in common with the single-neuron theory the notion that a single neuron may contain sufficient complexity to account for the complexity of a complete conscious experience.  It differs, however, in that the grandmother cell is assumed, in the context of explaining memory function, to be forever attached to a single experience, whereas the single neurons of the single-neuron theory flexibly change their experiences over time.  Among other consequences of this distinction, the limitations in processing capacity that plague grandmother cell models, referred to variously as the "combinatorial problem" (Singer and Gray 1995) or the "problem of exponential explosion" (Gold 1999), do not apply to the single-neuron theory.

 

     Zeki's Model:  Another single-neuron based model was described by William James (1890) in his discussion of what he called the "theory of polyzoism or multiple monadism."  According to this view, "every brain-cell has its own individual consciousness, which no other cell knows anything about, all individual consciousnesses being 'ejective' to each other."  Along similar lines, Zeki and Bartels (1999) have recently proposed that a primate visual brain consists of many separate functionally specialized processing systems comprising hierarchical nodes that each generate a "microconsciousness."  Neither of these schemes, however, explains how the individual microconsciousnesses are able to induce macroscopically evident behavior.  Zeki attempts to solve this problem by suggesting that the many microconsciousnesses participate in a higher order integrative activity that produces macroscopic consciousness.  This, however, returns us to the received view that consciousness is ultimately mediated by multiple neurons linked through dynamic mechanisms.  James, on the other hand, considers the possibility that a single pontifical arch-cell by itself might mediate the totality of one's consciousness but he goes on to assert that there is no evidence for such a single "center of gravity" in the brain.  Both authors appear to overlook the possibility that multiple separately and redundantly conscious neurons might summate their outputs so as to produce the illusion of a single mind.

 

     Bieberich's Model:  Two other investigators have published models featuring single-neuron consciousness.  Edwards (2005) proposes a model of individual neuron consciousness not unlike the one presented here, but he approaches the subject from the point of view of philosophy and physics and offers no specific neuroanatomical mechanisms.  Bieberich (2002), on the other hand, gives a detailed anatomical account of his single-neuron theory.  He proposes a theory of "recurrent fractal neural networks" in which spatial information encoded at the neuronal network level is reflected in the spatial activity patterns exhibited in the dendritic trees at the single neuron level.  As with the single-neuron theory proposed in the present paper, the individual neurons in Bieberich's model are assumed to mediate the entirety of a subject's conscious experience at a given moment.

 

     Bieberich's theory has certain difficulties, however.  First, he hypothesizes the existence of feedback connections from layer 5 pyramidal neurons to layer 2/3 pyramidal neurons in the construction of his theory.  However, feedback connections from layer 5 to layer 2/3 synapse on inhibitory interneurons, not on layer 2/3 pyramidal neurons (Dantzer and Callaway 2001; Thomson and Bannister 2003).  His theory would need to account for this.  Additionally, while he offers an intriguing anatomical mechanism to explain his theory at the microscopic level, he has yet to explain how it would function on the macroscopic level.  If these difficulties could be surmounted, Bieberich's theory might provide a possible complement to the microscopic mechanism proposed in this paper.

 

     The Single-Neuron Theory and the Binding Problem:  The most immediate implication of the single-neuron theory is that it provides a novel way of tackling the "binding problem," the problem of accounting for the apparent unity of conscious experience.  According to the currently popular view, which attempts to solve the binding problem in the context of an anatomically distributed model of consciousness, the expectation is that dynamic mechanisms, including those associated with 40 Hz gamma frequency electrical oscillations, will ultimately resolve the difficulty.  In contrast, the single-neuron theory offers an alternative possibility, that binding may be achieved by anatomical convergence.  That is, if it were true that single neurons were present that received the full complement of inputs that comprise conscious experience, then perceptual binding might be achievable on the basis of convergence of input upon individual neurons.  In such a model, topographical convergence rather than temporal synchrony would be invoked to explain perceptual unity.  Of course, a mechanism would then be needed to explain binding within neurons, which, as William James noted over a century ago (James 1890), are themselves aggregates of individual particles.  But at least the venue would be changed, with the search for a mechanism of binding being transferred from the neural network level to that within the single neuron.

 

     It must be noted that the above presupposes that the apparent unity of conscious perception is a real phenomena.  It is possible, instead, that, as has been asserted by Dennett (1991) and others, perceptual unity is an illusion.  It is possible, that is, that our apparently unified experience actually consists of independent fragments, and that the fragments never combine into a single experience.  If such were only partially the case, with the fragments being themselves composed of convergent elements, then there would still be a role for the single-neuron theory arrangement in explaining the partial convergence.  But if the extreme situation were present, in which each and every component of what seems to be a unified experience were associated with the activity of a different neuron, then the single-neuron theory proposed here would be rendered trivial.  Instead, Dennett's approach to explaining consciousness would suffice.

 

     What is needed to resolve this issue is a better understanding of the process of introspection, the principal source of our intuition that perceptual unity is real.  The nature of introspection is also relevant for addressing the question of how similar the conscious experiences of the PFC neurons have to be to one another for the single-neuron theory to be plausible.  It may be that the single neurons themselves lack self-awareness and base their identity solely on the feedback they receive pertaining to the neuronal population as a whole.  If that were the case, then a large variance in the conscious experiences of the PFC neurons might be compatible with a stable single-neuron model.  Until a comprehensive theory of introspection is available, however, deciding whether this is or is not the case will have to remain a matter of conjecture.

 

     Empirical Predictions:  There are a number of testable predictions made by the single-neuron theory, the presence of which serves to distinguish it from the competing network theories of consciousness.  At the macroscopic level, there is the question of whether lesions confined to the entirety of the left lateral PFC destroy VR-consciousness, and whether lesions confined to the entirety of lateral PFC bilaterally destroy higher consciousness more generally.  This is still an open question that needs directed empirical investigation.  At the microscopic level, the single-neuron theory makes the testable anatomical prediction that layer 5 pyramidal neurons will be found in the left lateral PFC that are individually the recipients of convergent axonal input derived from brain regions considered to be involved in the processing of the sensory, mnemonic, and emotional stimuli that compose VR-conscious content.  In addition, the theory makes the electrophysiological prediction that left lateral PFC neurons will be found that respond in single-cell recording experiments to combinations of sensory, emotional, and mnemonic stimuli that typify VR-conscious experience.  As noted above, direct single-cell recordings have already identified neurons in lateral PFC that respond selectively to conjoint visual and auditory stimuli (Aou et al. 1983), to conjoint visual, auditory and tactile stimuli (Tanila et al. 1992), and to conjoint object and location features (Rao et al. 1997).  The question is whether lateral PFC neurons exist that respond to conjoint input from the full complement of sensory, emotional, and mnemonic stimuli that comprise VR-conscious experience.  The demonstration of the existence of neurons that are suitably convergent, both anatomically and electrophysiologically, while expected with the single-neuron theory, would be difficult to justify within the network NCC framework.  Alternatively, the failure to identify such neurons despite a concerted effort to do so would militate against the single-neuron theory and favor the network approach.  Finally, there is the prediction that subgroups of lateral PFC neurons will be found that share similar spatially encoded dendritic inputs, and that the shape of those inputs will be found to be a homotopic, information preserving transform of the shape of neuronal activity at the population level.

 

     Speculative Implications:  As so far presented, the single-neuron theory does no more than shift the locus of the mind/brain interface from the neural network to the single neuron level of information processing.  It does not, by itself, offer any fundamental advance in tackling the philosophical difficulties inherent in the problem.  That is, the theory as so far presented does not take on the deeper questions of how and why individual neurons are imbued with experience.  It might, though, be possible to extend the theory in a way that does address these deeper questions.  In particular, since the mind/brain interface has been shifted to the level of the single neuron, certain physical processes, such as those involving electromagnetic fields or quantum mechanical phenomena, might be more plausibly invoked in theories of mind/brain interaction since the need to explain how these phenomena traverse synaptic spaces can be avoided.  Inclusion of such phenomena in evolving mind/brain theories might, in turn, offer a degree of flexibility that could allow such theories to address the deeper philosophical issues.

 

     As an example of how this might work, I offer the following brief speculative sketch derived from Whitehead's panpsychist approach to the hard problem of consciousness.  According to Whitehead (1933), the basic stuff of the universe is neither subjective experience nor objective matter but neutral "occasions" (or "events") that appear as either subjects or objects depending on one's observational perspective.  Thus, the same event is a subject with respect to the information it receives (that is, it "perceives" the incoming information) but is an object with respect to the information it emits (and which is subsequently "perceived" by other events).  In Whitehead's view, mind/brain duality is just a complex macroscopic instance of the perspectival duality that pervades all of nature.

 

     The difficulty with Whitehead's formulation is that it lacks a convincing explanation of how simple subject/object events combine to form complex subject/object entities such as those embodied by human minds.  Seager (1995) has called this difficulty the "combination problem."  Why do some configurations of matter (such as human brains) appear to serve as complex but unified subject/object entities, while others (such as rocks and thermostats) remain as mere aggregates of elementary subject/object events?  Seager suggests that only the synthetic nature of quantum mechanics, especially the phenomenon of quantum entanglement (in which spatially distributed occurrences are joined into a single event at the moment of reduction of the state vector), appears capable of resolving the combination problem.

 

     The possible involvement of quantum mechanics in consciousness has been summarily dismissed by many authors because the entanglement would, it is usually supposed, have to extend across macroscopic distances and involve spatially dispersed neuronal populations.  The difficulties imposed by such large distances, together with the lack of a plausible mechanism by which entanglement might "jump" across synapses, has been argued to render quantum mechanical theories of consciousness untenable (Grush and Churchland 1995).  With the single-neuron theory, however, the proposed entanglement would need to extend only throughout a portion of the dendritic tree of a single neuron.  While still a formidable proposition, the speculation that quantum mechanical effects might be relevant to consciousness might nevertheless gain in plausibility.

 

     The specific way that quantum entanglement could be invoked to explain consciousness would be as follows.  Suppose that within a small component of the dendritic tree of the neuron there were indivisible entangled events whose topology were reflective of the spatial patterns of electrical activity within larger portions of the dendritic tree.  It might be possible that such entangled events could individually be recipients of all the stimuli that go into conscious experience and be complex enough to match the complexity of conscious experience.  If such entangled events existed, then populations of them could conceivably summate their outputs and affect the neuron's outgoing message in a manner analogous to that in which, on the macroscopic level, populations of neurons are proposed to summate their outputs and affect macroscopic behavior.

 

     As has been noted by Bieberich, who has articulated an argument similar to the one being offered here (Bieberich 2002), entangled intraneuronal events could conceivably support a complexity of information processing comparable to that attributable to the whole brain in the neural network models.  Such entangled events could, in turn, act as indivisible Whiteheadean subject/object entities with subjective aspects serving as units of conscious experience and with objective aspects contributing to the information content of outgoing electrochemical messages at the axon hillock.  In the final model, then, a single subjective experience would correspond not to the activity of a single neuron but to the occurrence of a single entangled event within a single neuron.  In a logical extension of the single-neuron theory, an individual's brain would be construed as comprising a multiplicity of subjective experiences corresponding not just to the multitude of neurons within that brain, but to the larger number of entangled events within all those neurons.  In sum, the combination problem would be solved by avoiding altogether the need to combine the individual consciousnesses.

 

     This process would be assumed to be occurring not only in the right and left lateral prefrontal cortical neurons but in other neurons as well and even more generally throughout nature.  That is, every quantum mechanical event in the universe would be considered to manifest subjective/objective duality.  What would elevate a given quantum event to the status of a macroscopically observable conscious entity would be the manner in which it was coupled with input and output.  Thus, quantum mechanical events found generally in nature might well act as Whiteheadean subject/object entities but would have inputs and outputs reflecting only their immediate ultramicroscopic environments.  Events within the lateral PFC, in contrast, would be connected with inputs and outputs attached to happenings in the far-removed macroscopic world.

 

     There are, of course, serious challenges to this scheme, not the least of which is the need to demonstrate that there exist quantum mechanically entangled events spatially dispersed enough to invade at least several dendritic branches.  The assumptions are, however, empirically testable in principle.  The quantum mechanical model is offered, in any case, principally to illustrate how the transfer of the locus of mind/brain interaction to the intraneuronal domain might bring into play physical processes that might otherwise be regarded as irrelevant.

 

 

 

6. SUMMARY:

 

     A model of mind/brain interaction has been outlined that places the mind/brain interface at the level of single neurons or even at the level of single quantum events within neurons.  The scheme is built on the combined premise that there are populations of neurons for which individual neurons in the subgroup (1) receive all the types of inputs that characterize conscious experience, (2) are sufficiently complex on the input side to match the complexity of conscious experience, (3) produce complex output via interaction of conscious and ambient electrical spatial patterns within their dendritic trees, and (4) express that output in terms of the spatial pattern of their joint activation at the population level.  It is not argued that this model is necessitated by the available data, only that it is compatible with them.

 

     The theory is built upon a divergent/convergent feedforward model of information flow, both at the macroscopic and microscopic levels.  It is hypothesized on the macroscopic level that the lateral PFC serves as a region of convergence for all the inputs that feed conscious experience.  On the microscopic level, a variation of the synfire chain model is employed, in which successive pools of synchronously firing pyramidal neurons are hypothesized to exist that carry information in their spatial arrangement.  The theory provides a possible mechanism by which the spatial arrangement of synchronously firing pyramidal neurons can induce a corresponding spatial pattern of intradendritic electrical activity in the multiple neurons they target, and that the information contained in this pattern can be reexpressed by the spatial arrangement of a synchronously firing subgroup of the targeted neurons.  Amplification of selected synfire patterns may then occur in the context of attention, possibly involving gamma frequency oscillating cortico-thalamo-cortico loops.  Ultimately, further empirical investigation into the behavior of single neurons will determine whether the model is valid.  Toward this end, specific testable predictions have been delineated, rendering the theory potentially falsifiable and therefore of a legitimately scientific nature.

 

     The idea that single neurons might be individually conscious was originally considered by William James (1890) who went on, however, to note that "the cell is no more a unit, materially considered, than the total brain is a unit.  It is a compound of molecules, just as the brain is a compound of cells and fibers.  And the molecules, according to the prevalent physical theories, are in turn compounds of atoms."  A single-neuron theory would, then, still have to explain how the atoms of a neuron combine to form a single consciousness.  In response to this issue, and to illustrate the way in which the transfer of mind/brain interaction to the intraneuronal domain might facilitate the development of models of mind/brain interaction, a speculative sketch has been offered that is built upon Whitehead's panpsychist approach to the mind/brain problem.  Specifically, it is postulated that entangled quantum mechanical events within neurons might serve as indivisible Whiteheadean subject/object entities of sufficient complexity and connectedness to allow them to individually mediate the totality of conscious experience usually attributed to the brain as a whole.  The individual consciousnesses attached to single events would, in this picture, not combine but would instead remain independently conscious, thereby solving the combination problem by avoiding it altogether.  Such an extension of the single-neuron theory would, admittedly, be highly speculative but it would provide an example of how the theory might offer more than just an alternative model of cerebral information processing and go the further step of addressing the fundamental problem of mind/brain interaction itself.

 

ACKNOWLEDGMENTS:

 

     I wish to thank Josef Ashkenazi, PhD, Associate Professor of Physics, University of Miami, for his help in developing the ideas pertaining to quantum mechanics.  I also wish to thank Mohammad Rahat, David Wilson, Robert Fujimora, Mihai Preda, Gloria Peruyera, Nancy Tedone, David Chalmers, Todd Feinberg, and Kenneth Heilman for their valuable comments and suggestions.

 

REFERENCES:

 

Abeles M, 1991: Corticonics: Neural Circuits of the Cerebral Cortex. New York:

Cambridge University Press.

 

Abeles M, 2004: Time is precious. Science 304:523-524.

 

Adrian ED, 1926:      J Physiol 61:47.

 

Aertsen A, Diesmann M, Gewaltig MO, 1996: Propagation of synchronous spiking activity in feedforward neural networks. J Physiol Paris 90:243-247.

 

Agmon-Snir H, Carr CE, Rinzel J, 1998: The role of dendrites in auditory coincidence detection. Nature 393:268-272.

 

Alexander GE, Delong MR, Strick PL, 1986: Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9:357-381.

 

Alexander DM, Bourke PD, Sheridan P, Konstandatos O, Wright JJ, 2004: Intrinsic connections in tree shrew V1 imply a global to local mapping. Vision Research 44:857-876.

 

Alexander MP, Stuss DT, 2000: Disorders of frontal lobe functioning. Seminars in Neurology 20:427-437.

 

Amaral DG, Price JL, 1984: Amydalo-cortical projections in the monkey (Macaca fascicularis). J Comp Neurol 230:465-496.

 

Anderson JC, Binzegger T, Kahana O, Martin KAC, Segev I, 1999: Dendritic asymmetry cannot account for directional responses of neurons in visual cortex. Nature Neuroscience 2:820-824.

 

Aou S, Oomura Y, Hishino H, Ono T, Yambe K, Sikdar SK, Noda T, Inoue M, 1983: Functional heterogeneity of single neuronal activity in the monkey dorsolateral prefrontal cortex. Brain Res 260:121-124.

 

Arnoldi HM, Brauer W, 1996: Synchronization without oscillatory neurons. Biol Cybern 74:209-223.

 

Arnoldi HM, Englmeier KH, Brauer W, 1999: Translation-invariant pattern recognition based on Synfire chains. Biol Cybern 80:433-447.

 

Averbeck BB, Lee D, 2004: Coding and transmission of information by neural ensembles. Trends in Neurosciences 4:225-230.

 

Aviel Y, Pavlov E, Abeles M, Horn D, 2002: Synfire chain in a balanced network. Neurocomputing 44-46:285-292.

 

Baars BJ, 1988: A Cognitive Theory of Consciousness, Cambridge University Press.

 

Baars BJ, Franklin S, 2003: How conscious experience and working memory interact. Trends in Cognitive Sciences 7:166-172.

 

Baddeley A, 2003: Working memory: looking back and looking forward. Nature Reviews Neuroscience 4:829-839.

 

Baleydier C, Mauguiere F, 1980: The duality of the cingulated gyrus in monkey. Neuroanatomical study and functional hypothesis. Brain 103:525-554.

 

Barbas H, 1986: Pattern in the laminar origin of corticocortical connections. J Comp Neurol 252:415-422.

 

Barbas H, De Olmos J, 1990: Projections from the amygdala to basoventral and mediodorsal prefrontal regions in the rhesus monkey. J Comp Neurol 300:549-571.

 

Barbas H, Hilgetag CC, 2002: Rules relating connections to cortical structure in primate prefrontal cortex. Neurocomputing 44-46:301-308.

 

Barbas H, Pandya DN, 1989: Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. J Comp Neurol 286:353-375.

 

Barbas H, Rempel-Clower N, 1997: Cortical structure predicts the pattern of corticocortical connections. Cerebral Cortex 7:635-646.

 

Barlow HB, 1972: Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1:371-394.

 

Bates JF, Goldman-Rakic PS, 1993: Prefrontal connections of medial motor areas in the rhesus monkey. J Comp Neurol 336:211-228.

 

Baylis LL, Rolls ET, Baylis GC, 1995: Afferent connections of the caudolateral orbitofrontal cortex taste area of the primate. Neuroscience 64:801-812.

 

Bayne T, Chalmers DJ, 2003: What is the unity of consciousness? In: Cleeremans A (ed), The Unity of Consciousness: Binding, Integration, Dissociation. Oxford University Press: New York.

 

Bieberich E, 2002: Recurrent fractal neural networks: a strategy for the exchange of local and global information processing in the brain. BioSystems 66:145-164.

 

Binzegger T, Douglas RJ, Martin KAC, 2005: Axons in cat visual cortex are topologically self-similar. Cerebral Cortex 15:152-165.

 

Bialek W, Rieke F, de Ruyter van Steveninck RR, Warland D, 1991: Reading a neural code. Science 252: 1854-1857.

 

Bogen JE, 1995: On the neurophysiology of consciousness: Part II. Constraining the semantic problem. Consciousness and Cognition 4:137-158.

 

Borst A, Theunissen FE, 1999: Information theory and neural coding. Nature Neuroscience 2: 947-957.

 

Braver TS, Barch DM, Kelley WM, Buckner RL, Cohen NJ, Miezin FM, Snyder AZ, Ollinger JM, Akbudak E, Conturo TE, Petersen SE, 2001: Direct comparison of prefrontal cortex regions engaged by working and long-term memory tasks. Neuroimage 14:48-59.

 

Briggs F, Callaway EM, 2005: Laminar patterns of local excitatory input to layer 5 neurons in macaque primary visual cortex. Cerebral Cortex 15:479-488.

 

Broca P, 1861: Perte de la parole, remollissement chronique et destruction partielle du lobe anterieur gauche du cerveau. Bull Soc Anthropol 2:235-238.

 

Bruce C, Desimone R, Gross CG, 1981: Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. J Neurophysiol 46:369-384.

 

Bush G, Luu P, Posner MI, 2000: Cognitive and emotional influences in anterior cingulated cortex. Trends in Cognitive Science 4:215-222.

 

Campbell SR, Wang DL, Jayaprakash C, 1999: Synchrony and desynchrony in integrate-and-fire oscillators. Neural Comput 11:1595-1619.

 

Cannon RC, Wheal HV, Turner DA, 1999: Dendrites of classes of hippocampal neurons differ in structural complexity and branching patterns. Journal of Comparative Neurology 413:619-633.

 

Cash S, Yuste R, 1999: Linear summation of excitatory inputs by CA1 pyramidal neurons. Neuron 22:383-394.

 

Chalmers D, 1996: The Conscious Mind. New York: Oxford University Press.

 

Chavis DA, Pandya DN, 1976: Further observations on cortico-frontal connections in the rhesus monkey. Brain Res 117:369-386.

 

Cohen JD, Perlstein WM, Braver TS, Nystrom LE, Noll DC

 

Conde F, Lund JS, Lewis DA, 1996: The hierarchical development of monkey visual cortical regions as revealed by the maturation of parvalbumin-immunoreactive neurons. Developmental Brain Research 96:261-276.

 

Constantinidis C, Franowicz, Goldman-Rakic PS, 2001: Coding specificity in cortical microcircuits: a multiple-electrode analysis of primate prefrontal cortex. The Journal of Neuroscience 21:3646-3655.

 

Constantinidis C, Goldman-Rakic PS, 2002: Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J Neurophysiol 88:3487-3497.

 

Crick F, Koch C, 1990a: Towards a neurobiological theory of consciousness. Seminars in Neuroscience 2:263-275.

 

Crick F, Koch C, 1990b: Some reflections on visual awareness. Cold Spring Harbor Symp Quant Biol 55:953-962.

 

Crick F, 1994: The Astonishing Hypothesis. Simon and Schuster: New York.

 

Crick F, Koch C, 1998: Consciousness and Neuroscience. Cerebral Cortex 8:97-107.

 

Dahaene S, Kerszberg M, Changeux JP, 1998: A neuronal model of a global workspace in effortful cognitive tasks. PNAS 95:14529-14534.

 

Damasio AR, 1998: Investigating the biology of consciousness. Phil Trans R Soc Lond B 353:1879-1882.

 

Damasio AR, 1999: The Feeling of What Happens. Harcourt Brace and Company: New York.

 

Dandy WE, 1946: The location of the conscious center in the brain – the corpus striatum. Bull Johns Hopkins Hosp 79:34-58.

 

Dantzker JI, Callaway EM, 2000: Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons. Nature Neuroscience 3:701-707.

 

Deacon TW, 1992: Cortical connections of the inferior arcuate sulcus cortex in the macaque brain. Brain Research 573:8-26.

 

Dehaene S, Kerszberg M, Changeux JP, 1998: A neuronal model of a global workspace in effortful cognitive tasks. Proc Nat Acad Sci 95:14529-14534.

 

Dennett DC, 1991: Consciousness Explained. Boston: Little, Brown and Company.

 

D'Esposito M, Aguirre GK, Zarahn E, Ballard D, Shin RK, Lease J, 1998: Functional MRI studies of spatial and nonspatial working memory. Cognitive Brain Research 7:1-13.

 

Diesmann M, Gewaltig MO, Aertsen A, 1999: Stable propagation of synchronous spiking in cortical neural networks. Nature 402:529-533.

 

Douglas RF, Martin KAC, 2004: Neuronal circuits of the neocortex. Annu Rev Neurosci 27:419-451.

 

Durstewitz D, Seamans JK, Sejnowski TJ, 2000: Neurocomputational models of working memory. Nature Neuroscience Suppl 3: 1184-1191.

 

Edelman GM, 1993: Neural darwinism - selection and reentrant signaling in higher brain-function. Neuron 10:115-125.

 

Edelman GM and Tononi G, 2000: A Universe of Consciousness: How Matter Becomes Imagination. New York: Basic Books.

 

Edwards JCW, 2005: Is consciousness only a property of individual cells? J Cons Studies (in press).

 

Elston GN, 2003: Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. Cerebral Cortex 13:1124-1138.

 

Engel AK, Fries P, Konig P, Brecht M, Singer W, 1999: Temporal binding, binocular rivalry, and consciousness. Consciousness and Cognition 8:128-151.

 

Eslinger PJ, Damasio AR, 1985: Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR. Neurology 35:1731-1741.

 

Felleman DJ, Van Essen DC, 1991: Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex 1:1-47.

 

Fletcher PC, Henson RNA, 2001: Frontal lobes and human memory: insights from functional neuroimaging. Brain 124:849-881.

 

Fries P, Reynolds JH, Rorie AE, DesimoneR, 2001: Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560-1563

 

Foxe JJ, Simpson GV, 2002: Flow of activation from V1 to frontal cortex in humans. A framework for defining "early" visual processing. Exp Brain Res 142:139-150.

 

Frick A, Magee J, Johnston D, 2004: LTP is accompanied by an enhanced local excitability of pyramidal neuron dendrites. Nat Neurosci 7:126-35

Froemke RC, Pou MM, Dan Y, 2005: Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 43:221-225.

 

Funahashi S, Inoue M, 2000: Neuronal interactions related to working memory processes in the primate prefrontal cortex revealed by cross-correlation analysis. Cerebral Cortex 10:533-551.

 

Fuster JM, 1998: Linkage at the top. Neuron 21:1223-1229.

 

Fuster JM, 1999: Memory in the cerebral cortex. MIT Press: Cambridge.

 

Fuster JM, Alexander GE, 1971: Neuron activity related to short-term memory. Science 173:652-654.

 

Geschwind N, 1981: The perverseness of the right hemisphere. The Behavioral and Brain Sciences 4:106-107.

 

Gewaltig MO, Diesmann M, Aertsen S, 2001: Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. Neural Networks 14:657-673.

 

Gilbert CD, 1998: Adult cortical dynamics. Physiological Reviews 78:467-485.

 

Gilbert CD, Wiesel TN, 1986: Intrinsic connectivity and receptive field properties in visual cortex. Vision Res 25:365-374.

 

Gold I, 1999: Does 40-Hz oscillation play a role in visual consciousness? Consciousness and Cognition 8:186-195.

 

Golding NL, Kath WL, Spruston N, 2001: Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites. J Neurophysiol 10:2998-3010.

 

Goldman PS, Nauta WJ, 1976: Autoradiographic demonstration of a projection from prefrontal association cortex to the superior colliculus in the rhesus monkey. Brain Res 116:145-149.

 

Goldman-Rakic PS, 1992: Working memory and the mind. Scientific American September.

 

Goldman-Rakic PS, Schwartz ML, 1982: Interdigitation of contralateral and ipsilateral columnar projections to frontal association cortex in primates. Science 216:755-757.

 

Goldman-Rakic PS, Selemon LD, Schwartz ML, 1984: Dual pathways connecting the dorsolateral prefrontal cortex with the hippocampal formation and parahippocampal cortex in the rhesus monkey. Neuroscience 12:719-743.

 

Gonzalez-Burgos G, Barrionuevo G, Lewis DA, 2000: Horizontal synaptic connections in monkey prefrontal cortex: an in vitro electrophysiological study. 10:82-92.

 

Goodale MA, Milner AD, 1992: Separate visual pathways for perception and action. Trends Neurosci 15:20-25.

 

Grammont F, Riehle A, 2003: Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time. Biological Cybernetics 88:360-373.

 

Gray CM, 1999: The temporal correlation hypothesis of visual feature interaction: still alive and well. Neuron 24:31-47.

 

Gray JR, Braver TS, Raichle ME, 2002: Integration of emotion and cognition in the lateral prefrontal cortex. PNAS 99:4115-4120.

 

Grossberg S, Mingolla E, Pack C, 1999: A neural model of motion processing and visual navigation by cortical area MST. Cerebral Cortex 9:878-895.

 

Grush R, Churchland PS, 1995: Gaps in Penrose's toilings. J of Consciousness Studies 2:10-29.

 

Hardcastle V, 1994: Psychology's binding problem and possible neurobiological solutions. J of Consciousness Studies 1:66-90.

 

Hardcastle V, 1997: Consciousness and the neurobiology of perceptual binding. Seminars in Neurology 17:163-170.

 

Hausser M, 2001: Synaptic function: dendritic democracy. Curr Biol 11:R10-R12.

 

Hausser M, Major G, Stuart GJ, 2001: Differential shunting of EPSPs by action potentials. Science 291:138-141.

 

Hausser M, Mel B, 2003: Dendrites: bug or feature? Current Opinions in Neurobiology 13:372-383.

 

Heilman KM. and Valenstein EV, 1993: Clinical Neuropsychology. New York: Oxford University Press.

 

Helmchen F, Svoboda K, Denk W, Tank DW, 1999: In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nature Neuroscience 2:989-996.

 

Hertz J, Prugel-Bennett A, 1996: Learning synfire chains: turning noise into signal. Int J Neural Syst 7:445-450

 

Hoffman DA, Magee JC, Colbert CM, Johnston D: Potassium channel regulation of signal propagation in dendrites of hippocampal pyramidal neurons. Nature 38:869-875.

 

Hopfield JJ, 1982: Neural networks and physical systems with emergent collective computational abilities. PNAS 79:2554-2558.

 

Hopfield JJ, Herz AVM, 1995: Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons. PNAS 92:6655-6662.

 

Hubel DH, Wiesel TN, 1962: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol (Lond) 160:106-154.

 

Hubel DH, Wiesel TN, 1965: Receptive fields and functional architecture in two non-striate areas (18 and 19) of the cat. J Neurophysiol 28:229-289.

 

Ikegaya Y, Aarn G, Cossart R, Aronov D, Lampl I, Ferster D, Yuste R: Synfire chains and cortical songs: temporal modules of cortical activity. Science 304:559-564.

 

Inui K, Wang X, Tamura Y, Kaneoke Y, Kakigi R, 2004: Serial processing in the human somatosensory system. Cerebral Cortex 14:851-857.

 

Ishai A, Ungerleider LG, Haxby JV, 2000: Distributed neural systems for the generation of visual images. Neuron 28:979-990.

 

Izhikevich EM, Gally JA, Edelman GM, 2004: Spike-timing dynamics of neuronal groups. Cerebral Cortex 14:933-944.

 

Jacobs GA, Theunissen FE, 2000: Extraction of sensory parameters from a neural map by primary sensory interneurons. The Journal of neuroscience 20:2934-2943.

 

James W, 1890: Principles of Psychology. Cambridge: Harvard University Press.

 

John ER, 2001: A field theory of consciousness. Consciousness and Cognition 10:184-213.

 

Johnson DMG, Illig KR, Behan M, Haberty LB, 2000: New features of connectivity in piriform cortex visualized by intracellular injection of pyramidal cells suggest that "primary" olfactory cortex functions like "association" cortex in other sensory systems. The Journal of Neuroscience 20:6974-6982.

 

Jones EG, 2002: Thalamic circuitry and thalamocortical synchrony. Phil Trans R Soc Lond B 357:1659-1673.

 

Jones EG, Powell TPS, 1970: An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93:793-820.

 

Koch C, 1999: Biophysics of Computation: Information Processing in Singe Neurons, Oxford University Press: Oxford.

 

Koch C, 1997: Computation and the single neuron. Nature 385:207-210.

 

Kjaer TW, Nowak M, Kjaer KW, Lou AR, Lou HC, 2001: Precuneus-prefrontal activity during awareness of visual verbal stimuli. Consciousness and Cognition 10:356-365.

 

Konig P, Engel AK, Roelfsema PR, Singer W, 1995: How precise is neural synchronization? Neural Computation 7:469-485.

 

Kritzer MF, Goldman-Rakic PS, 1995: Intrinsic circuit organization of the major layers and sublayers of the dorsolateral prefrontal cortex in the rhesus monkey. J Comp Neurol 359:131-143.

 

Kubota K, Niki H, 1971: Prefrontal cortical unit activity and delayed alternation performance in monkeys. J Neurophysiol 34:337-347.

 

Kuypers HGJM, Szwarcbart MK, Mishkin M, 1965: Occipitotemporal cortico-cortical connections in the rhesus monkey.  Expl Neurol 11:245.

 

Lamme VAF, Roelfsema PR, 2000: The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences 23:571-579.

 

Larkum ME, Zhu JJ, Sakmann B, 1999a: A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398:338-341.

 

Larkum ME, Kaiser KMM, Sakmann B, 1999b: Calcium electrogenesis in distal apical dendrites of layer 5 pyramidal cells at a critical frequency of back-propagating action potentials. Proc Natl Acad Sci USA 96:14600–14604.

 

Larkum ME, Zhu JJ, Sakmann B, 2001: Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. J Physiol 2001, 533:447-466.

 

Larkum ME, Senn W, Luscher HR, 2004: Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cerebral Cortex 14:1059-1070.

 

Larkum ME, Zhu JJ, 2002: Signaling of layer 1 and whisker-evoked Ca2+ and Na+ action potentials in distal and terminal dendrites of rat neocortical pyramidal neurons in vitro and in vivo. J Neurosci 22:6991-7005.

 

Lachaux JP, Rodriguez E, Martinerie J, Varela FJ, 1999: Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194-208.

 

Laureys S, Lemaire C, Maquet P, 1999: Cerebral metabolism during vegetative state and after recovery to consciousness. J Neurol Neurosurg Psychiatry 67:121-133.

 

Laureys S, 2004: Functional neuroimaging in the vegetative state. NeuroRehabilitation 19:335-341.

 

Livingstone MS, 1998: Mechanisms of direction selectivity in macaque V1. Neuron 20:509-526.

 

Llinas R, Ribary U, Contreras D, Pedroarena C, 1998: The neuronal basis for consciousness. Phil Trans R Soc Lond B 353:1841-1849.

 

Lu MT, Preston JB, Strick PL, 1994: Interconnections between the prefrontal cortex and the premotor areas in the frontal lobe. J Comp Neurol 341:375-392.

 

Lubke J, Markram H, Frotscher M, Salmann B, 1996: Frequency and dendritic distribution of autapses established by layer 5 pyramidal neurons in the developing rat neocortex: comparison with synaptic innervation of adjacent neurons of the same class. J Neurosci 10:3209-3218.

 

Lumer ED, Edelman GM, Tononi G, 1997: Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing. Cereb Cortex 7:228-236.

 

MacGregor RJ, Ascarrunz FG, Kisley MA, 1995: Characterization, scaling, and partial representation of neural junctions and coordinated firing patterns by dynamic similarity. Biol Cybern 73:155-166.

 

Magee JC, 2000: Dendritic integration of excitatory synaptic input. Nature Reviews Neuroscience 1:181-190.

 

Magee JC, 1999: Dendritic Ih normalizes temporal summation in hippocampal CA1 neurons. Nat Neurosci 2:508-514

 

Magee JC, Cook EP, 2000: Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons. Nature Neuroscience 3:895-903.

 

Mainen ZF, Sejnowski TJ, 1996: Influence of dendritic structure on firing pattern inn model neocortical neurons. Nature 382:363-366.

 

Marsalek P, Koch C, Maunsell J, 1997: On the relationship between synaptic input and spike output jitter in individual neurons. PNAS 94:735-740.

 

Maunsell JHR, Van Essen DC: The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. The Journal of Neuroscience 2563-2586.

 

McCullough W, Pitts W, 1943: A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115-133.

 

McIntosh AR, Rajah MN, Lobaugh NJ, 1999: Interactions of prefrontal cortex in relation to awareness in sensory learning. Science 284:1531-1533.

 

Melchitzky DS, Gonzalez-Burgos G, Barrionuevo G, Lewis DA, 2001: Synaptic targets of the intrinsic axon collaterals of supragranular pyramidal neurons in monkey prefrontal cortex. J Comp Neurol 430:209-221.

 

Miller R, 1996: Cortico-thalamic interplay and the security of operation of neural assemblies and temporal chains in the cerebral cortex. Biol Cybern 75:263-275.

 

Miller EK, Cohen JD, 2001: An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167-202.

 

Milner AD, Goodale MA, 1995: The Visual Brain in Action. Oxford University Press: Oxford.

 

Milner AD Goodale MA, 1993: Visual pathways to perception and action. Prog Brain Res 95:317-337.

 

Mitchell BD, Cauller LJ, 2001: Corticocortical and thalamocortical projections to layer 1 of the frontal neocortex in rats. Brain Res 921:68-77.

 

Mitchell BD, Macklis JD, 2005: Large-scale maintenance of dual projections by callosal and frontal cortical projection neurons in adult mice. J Comp Neurol 482:17-32.

 

Neltner L, Hansel D, Mato G, Meunier C, 2000: Synchrony in heterogeneous networks of spiking neurons. Neural Comput 12:1607-1641.

 

Niebur E, Hsiao SS, Johnson KO, 2002: Synchrony: a neuronal mechanism for attentional selection? Current Opinions in Neurobiology 12:190-194.

 

Niebur E, Koch C, 1994: A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons. J Comput Neurosci 1:141-158.

 

Niebur E, Koch C, Rosin C, 1993: An oscillation-based model for the neural basis of attention. Vision Res 33:2789-2802.

 

Nirenberg S, Latham PE, 2003: Decoding neuronal spike trains: How important are correlations? PNAS 100:7348-7353.

 

Nowotny T, Huerta R, 2003: Explaining synchrony in feed-forward networks: are McCulloch-Pitts neurons good enough? Biol Cybern 89:237-241.

 

Oliveri M, Purriziani P, Carlesimo GA, Koch G, Tomaiuolo F, Panella M, Caltagirone C, 2001: Parieto-frontal interactions in visual-object and visual-spatial working memory: evidence from transcranial magnetic stimulation. Cerebral Cortex 11:606-618.

 

Orpwood RD, 1994: A possible neural mechanism underlying consciousness based on the pattern processing capabilities of pyramidal neurons in the cerebral cortex. J Theor Biol 169:403-418.

 

Pandya DN, Kuypers HG, 1969: Cortico-cortical connections in the rhesus monkey. Brain Research 13:13-48.

 

Pandya DN, Yeterian EH, 1990: Prefrontal cortex in relation to other cortical areas in rhesus monkey - architecture and connections. Prog Brain Res 85:63-94.

 

Parvizi J, Damasio A, 2001: Consciousness and the brainstem. Cognition 49:135-159.

 

Petrides M, Pandya DN, 1984: projections to the frontal cortex from the posterior parietal region in the rhesus monkey. J Comp Neurol 228:106-116.

 

Petrides M, Pandya DN, 1999: Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque  brain and corticocortical connection pattern. Eur J Neurosci 11:1011-1036.

 

Phan KL, Wager T, Taylor SF, Liberzon I, 2002: Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16:331-348.

 

Pinsky PF, Rinzel J, 1995: Synchrony measures for biological neural networks. Biol Cybern 73:129-137.

 

Poirzai P, BrannonT, Mel BW, 2003: Pyramidal neuron as two-layer neural network. Neuron 37:989-999.

 

Polsky A, Mel BW, Schiller J, 2004: Computational subunits in thins dendrites of pyramidal cells. Nature Neuroscience 7:621-627.

 

Porrino LJ, Crane AM, Goldman-Rakic PS, 1981: Direct and indirect pathways from the amygdala to the frontal lobe in rhesus monkeys. J Comp Neurol 198:121-136.

 

Porter R, Ghosh S, Lange GD, Smith TG, 1991: A fractal analysis of pyramidal neurons in mammalian motor cortex. Neuroscience letters 130:112-116.

 

Postma EO, van den Herik HJ, Hudson PT, 2996: Robust feedforward processing in synfire chains. Int J Neural Syst 7:537-542.

 

Pouget A, Latham P, 2002: Digitalized neural networks:  long-term stability from forgetful neurons. Nature Neuroscience 5:709-710.

 

Prut Y, Vaadia E, Bergman H, Haalman I, Slovin H, Abeles M, 1998: Spatiotemporal structure of cortical activity: properties and behavioral relevance. J Neurophysiol 79:2857-2874.

 

Puccetti R, 1973: Brain bisection and personal identity. British Journal of Philosophy of Science 24:339-355.

 

Raichle ME, 2000: The neural correlates of consciousness: an analysis of cognitive skill learning. In Gazzaniga MS: The New Cognitive Neurosciences, Second Edition, MIT Press: Cambridge.

 

Rainer G, Asaad WF, Miller EK, 1998: Memory fields of neurons in the primate prefrontal cortex. PNAS 95:15008-15013.

 

Rainer G, Rao SC, Miller EK, 1999: Prospective coding for objects in the primate prefrontal cortex. J Neurosci 19:5493-5505.

 

Rall W, 1959: Branching dendritic trees and motoneuron membrane resistivity. Exp Neurol 1:491-527.

 

Rall W, 1967: Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology 30:1138-1168.

 

Rammont F, Riehle A, 2003: Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time. Biol Cybernetics 88:30-373.

 

Rao SC, Rainer G, Miller EK, 1997: Integration of what and where in the primate prefrontal cortex. Science 276:821-824.

 

Rempel-Clower NL, Barbas H, 2000: The laminar pattern of connections between prefrontal and anterior temporal cortices in the rhesus monkey is related to cortical structure and function. Cerebral Cortex 10:851-865.

 

Revonsuo A, 1999: Binding and the phenomenal unity of consciousness. Consciousness and Cognition 8:178-185.

 

Reyes AD, 2003: Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nature Neurosci 6: 593-599.

 

Reynolds JH, Desimone R, 1999: The role of neural mechanisms of attention in solving the binding problem Neuron 24:19-29.

 

Robertson LC, 2003: Binding, spatial attention and perceptual awareness. Nature Reviews Neuroscience 4:93-102.

 

Rockland KS, Pandya DN, 1979: Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Research 179:3-20.

 

Rolls ET, France L, Aggelopoulos NC, Reece S, 2003: An information theoretic approach to the contributions of the firing rates and the correlations between the firing of neurons. J Neurophysiol 89:2810-2822.

 

Romo R, Brody CD, Hernandez A, Lemus L, 1999: Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399:470-473.

 

Rudd ME, Brown LG, 1997: Noise adaptation in integrate-and-fire neurons. Neural Comput 9:1047-1069.

 

Sato M, Kuroda R, Ioku M, Kim A, Tanaka S, Nakakita K, Kohama A, Fugii C, Ono S, 1989: Regional cerebral blood flow in the resistant vegetative state. Neurol Med Chir 29:389-394.

 

Sauve K, 1999: Gamma-band synchronous oscillations: recent evidence regarding their functional significance. Consciousness and Cognition 8:213-224.

 

Schaefer AT, Larkum ME, Sakmann B, Roth A, 2003: Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J Neurophysiol  89:3143–3154.

 

Schiller J, Helmchen F, Sakmann B, 1997: Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons. J Physiol 505:605-616.

 

Schiller J, Schiller Y, Stuart G, Sakmann B, 1997: Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons. J Physiol 505, 605–616.

 

Schmahmann JD, Pandya DN, 1997: Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey. J Neurosci 17:438-458.

 

Schubert D, Staiger JF, Cho N, Kotter R, Zilles K, Luhmann HJ, 2001: Layer-specific intracolumnar and transcolumnar functional connectivity of layer V pyramidal cells in rat barrel cortex. The Journal of Neuroscience 21:3580-3592.

 

Schuz A, 1998: Neuroanatomy in a computational perspective. In Arbib MA: Handbook of Brain Theory and neural Networks.  MIT Press: Cambridge.

 

Schwartz EL, 1980: Computational anatomy and functional architecture of striate cortex: a spatial mapping approach to perceptual coding. Vision Research 20:645-669.

 

Schwindt P, Crill W, 1999: Mechanisms underlying burst and regular spiking evoked by

dendritic depolarization in layer 5 cortical pyramidal neurons. J Neurophysiol 81:1341–1354.

 

Seltzer B, Pandya DN, 1989: Frontal lobe connections of the superior temporal sulcus in rhesus monkey. J Comp Neurol 281:97-1113.

 

Shadlen MN, Newsome WT, 1994: Noise, neural codes and cortical organization. Curr Opin Neurobiol 4:569-579.

 

Shadlen MN, Newsome WT, 1998: The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18:3870-3896.

 

Shadlen MN, Movshon JA, 1999: Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24:67-77.

 

Seager W, 1995: Consciousness, information and panpsychism. Journal of Consciousness Studies 2:272-288.

 

Segev I, London M, 2000: Untangling dendrites with quantitative models. Science 290:744-750.

 

Segev I, 2003: Synchrony is stubborn in feedforward cortical networks. Nature Neurosci 6:543-544.

 

Sewards TV, Sewards MA, 2001: On the correlation between synchronized oscillatory activities and consciousness. Consciousness and Cognition 10:485-495.

 

Singer W, 1998: Consciousness and the structure of neuronal representations. Phil Trans R Soc Lond B 353:1829-1840.

 

Singer W, 2001: Consciousness and the binding problem. Ann NY Acad Sci 929:123-146.

 

Singer W, Gray CM, 1995: Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci 18:555-586.

 

Slotnick SD, Moo LR, Segal JB, Hart J, 1969: Distinct prefrontal cortex activity associated with item memory and source memory for visual shapes. Cognitive Brain Research 17:75-82.

 

Softky WR, Koch C, 1993: The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. The Journal of Neuroscience 13:334-350.

 

Sougné JP, French RM, 2001: Synfire chains and catastrophic interference. In JD Moore and K Stenning (Eds.) Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society. Pp. 970-975. Mahwah,NJ: Lawrence Erbaum Ass.

 

Sperry RW, 1969: A modified concept of consciousness. Psychological Reviews 26:532-536.

 

Spratling MW, 2002: Cortical region interactions and the functional role of apical dendrites. Behavioral and Cognitive Neuroscience Reviews 1:219-228.

 

Spruston N, Schiller Y, Stuart G, Sakmann B, 1995: Activity-dependent action potential invasion and calcium influx into hippocampal CA1 dendrites. Science 286:297-300.

 

Spruston N, 2000: Distant synapses raise their voices. Nature Neuroscience 3:849-895.

 

Steinmetz PN, Roy A, Fitzgerald PJ, Hsiao SS, Johnson KO, Niebur E, 2000: Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404:187-190.

 

Stephan KM, Thaut MH, Wunderlich G, Schicks W, Tian B, Tellmann L, Schmitz T, Herzog H, McIntosh C, Seitz RJ, Homberg V, 2002: Conscious and subconscious sensorimotor synchronization - prefrontal cortex and the influence of awareness. Neuroimage 15:345-352.

 

Stevens CF, Zador AM, 1998: Input synchrony and the irregular firing of cortical neurons. Nature Neuroscience 1:210-217.

 

Stuart GJ, Sakmann B, 1994: Active propagation of somatic action potentials

into neocortical pyramidal cell dendrites. Nature 367:69–72.

 

Tanila H, Carlson S, Linnankoski I, Lindroos F, Kahila H, 1992: Functional properties of dorsolateral prefrontal cortical neurons in awake monkey. Behav Brain Res 47:169-180.

 

Taylor JG, 2001: The central role of the parietal lobes in consciousness. Consciousness and Cognition 10:379-417.

 

Thomson AM, Bannister AP, 1998: Postsynaptic pyramidal target selection by descending layer III pyramidal axons:  dual intracellular recordings and biocytin filling in slices of rat neocortex. Neuroscience 3:669-683.

 

Thomson AM, Bannister AP, Mercer A, Morris OT, 2002a: Target and temporal pattern selection at neocortical synapses. Phil Trans R Soc Lond B 357:1781-1791.

 

Thomson AM, West DC, Wang Y, Bannister AP, 2002b: Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 22-5 of adult rat and cat neocrotex: triple intracellular recordings and biocytin labelling in vitro. Cerebral Cortex 12:936-953.

 

Thomson AM, Bannister AP, 2003: Interlaminar connections in the neocortex. Cerebral Cortex 13:5-17.

 

Thorpe S, 1998:  Localized versus distributed representations.  In: Arbib MA: Handbook of Brain Theory and Neural Networks. MIT Press: Boston.

 

Tulving E, 2002: Episodic memory: from mind to brain. Annu Rev Psychol. 2002;53:1-25.

 

Ungerleider LG, Mishkin M, 1982: Two cortical visual systems. In Analysis of Visual Behavior, DJ Ingle, MA Goodale, RJW Mansfield, eds. (Cambridge, MA: MIT Press).

 

Usrey WM, Reid RC, 1999: Synchronous activity in the visual system. Annu Rev Physiol 61:435-456.

 

Van Essen DC, Gallant JL, 1994: Neural mechanisms and motion processing in primate visual system. Neuron 13:1-10.

 

Van Ooyen A, Duijnhouwer J, Remme MWH, van Pelt J, 2002: The effect of dendritic topology on firing patterns in model neurons. Network Computation iin Neural Systems 13: 311-325.

 

Vetter P, Roth A, Hausser M, 2001: Propagation of action potentials in dendrites depends on dendritic morphology. J Neurophysiol 85:926-937.

 

Wei DS, Mei YA, Bagal A, Kao JP, Thompson SM, Tang CM, 2001: Compartmentalized and binary behavior of terminal dendrites in hippocampal pyramidal neurons. Science 293:2272-2275.

 

Weinberg RJ, Pierce JP, Rustioni A, 1990: Single fiber studies of ascending input to the cuneate nucleus of cats: I. Morphometry of primary afferent fibers. J Comp Neurol 300:113-133.

 

Wheeler MA, Stuss DT, Tulving E, 1995: Frontal lobe damage produces episodic memory impairment. Journal of the International Neuropsychological Society 1:525-536.

 

Whitehead AN, 1933: Adventures of Ideas. New York: The Free Press.

 

Williams SR, Stuart GJ, 2000: Backpropagation of physiological spike trains. J Neurosci 20:8238-8246.

 

Williams SR, Stuart GJ, 2002: Dependence of EPSP efficacy on synapse location in neocortical pyramidal neurons. Science 295:1907-1910.

 

Wolfe JM, Cave KR, 1999: The psychophysical evidence for a binding problem in human vision. Neuron 24:11-17.

 

Yazdanbakhsh A, Babadi B, Rouhani S, Arabzadeh E, Abbassian A, 2002: New attractor states for synchronous activity in synfire chains with excitatory and inhibitory coupling. Biol Cybern 86:367-378.

 

Zeki S, Bartels A, 1999: Toward a theory of visual consciousness. Consciousness and Cognition 8:225-259.



[1] The designation "left hemisphere" will be used to refer to the "dominant" or "language" hemisphere, located on the left side of the brain in most people.