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General Principles for Brain Design

Josephson, Brian D. (2005) General Principles for Brain Design. [Conference Paper] (In Press)

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Abstract

The task of understanding how the brain works has met with only limited success since important design concepts are not as yet incorporated in the analysis. Relevant concepts can be uncovered by studying the powerful methodologies that have evolved in the context of computer programming, raising the question of how the concepts involved there can be realised in neural hardware. Insights can be gained in regard to such issues through the study of the role played by models and representation. These insights lead on to an appreciation of the mechanisms underlying subtle capacities such as those concerned with the use of language. A precise, essentially mathematical account of such capacities is in prospect for the future.

Item Type:Conference Paper
Additional Information:PACS: 87.19.La. After it is published, this paper will be found at http://proceedings.aip.org/proceedings.
Keywords:brain design, language, abstraction, representation, models, neurocomputational science
Subjects:Neuroscience > Neural Modelling
Computer Science > Complexity Theory
ID Code:4650
Deposited By:Josephson, Prof. Brian D.
Deposited On:19 Dec 2005
Last Modified:12 Sep 2007 18:01

References in Article

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1. S. R. Quartz and T. J. Sejnowski, The neural basis of cognitive development: A constructivist manifesto, Behavioral and Brain Sciences, 20(4) 537-596 (1997).

2. The JAVA Tutorial, http://java.sun.com/docs/books/tutorial/java/concepts/ (2005)

3. N. A. Baas; "Emergence, Hierarchies and Hyperstructures,” in Artificial Life III edited by C.G. Langton, Redwood City, CA: Addison-Wesley, 515-537 (1994).

4. A. Karmiloff-Smith, Beyond Modularity: a Developmental Perspective on Cognitive Science, Cambridge, MA: MIT Press (1992).

5. R. Jackendoff, Foundations of Language, Oxford: Oxford (2002).

6. M. Arbib, “The Mirror System, Imitation, and the Evolution of Language,” in Imitation in Animals and Artifacts, edited by C. Nehaniv and K. Dautenhahn (2000), Cambridge, MA: MIT Press.

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