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Intelligent encoding and economical communication in the visual stream.

Lorincz, Andras (2004) Intelligent encoding and economical communication in the visual stream. [Conference Poster] (Unpublished)

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Abstract

The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for communicating agents using Hebbian learning and working on hard combinatorial problems, which are easy to verify. Computational definition of the concept of intelligence is provided. Simulations illustrate the idea.

Item Type:Conference Poster
Keywords:neocortex, hippocampus, generative networks, NP-hard problems, collaboration, agents, visual stream
Subjects:Neuroscience > Neural Modelling
Computer Science > Machine Learning
ID Code:3505
Deposited By:Lorincz, Prof Andras
Deposited On:18 Mar 2004
Last Modified:12 Sep 2007 17:51

References in Article

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B. Szatmary, B. Poczos and A. Lorincz, 2004, J. Physiol. (Paris), accepted.

D. D. Lee and H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature 1999, 401: 788-791.

D. A. Henze, L. Wittner and G. Buzsaki, Single granule cells reliably discharge targets in the hippocampal CA3 network in vivo, Nature Neurosci. 2002, 5: 790-795.

A. V. Egorov, B. N. Hamam, E. Fransen, M. E. Hasselmo and A. A. Alonso, Graded persistent activity in entorhinal cortex neurons, Nature, 2002, 420: 173-178.

I. Biederman, Recognition-by-components: a theory of human image understanding, Psychol. Rev., 1987, 94: 115--147

A. Lorincz, B. Szatmary and G. Szirtes, Mystery of structure and function of sensory processing areas of the neocortex: A resolution, J. Comp. Neurosci., 2002, 13: 187-205.

S. Harnad, Can a Machine Be Conscious? How? 2003, http://cogprints.ecs.soton.ac.uk/archive/00002460/.

A. Hyvarinen, Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation, Neural Computation, 1999, 11: 1739--1768.

A. Lorincz, Towards a unified model of cortical computation II}: From control architecture to a model of consiousness, Neural Network World, 1997, 7: 137-152.

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