Coherent Infomax as a Computational Goal for Neural Systems

Signal processing in the cerebral cortex is thought to involve a common multi-purpose algorithm embodied in a canonical cortical micro-circuit that is replicated many times over both within and across cortical regions. Operation of this algorithm produces widely distributed but coherent and relevant...

Full description

Saved in:
Bibliographic Details
Published in:Bulletin of mathematical biology 2011-02, Vol.73 (2), p.344-372
Main Authors: Kay, Jim W, Phillips, W. A
Format: Article
Language:English
Subjects:
Algorithms
Animals
Bayes Theorem
Cell Biology
Cerebral Cortex - physiology
Humans
Information Theory
Life Sciences
Mathematical and Computational Biology
Mathematics
Mathematics and Statistics
Models, Neurological
Monte Carlo Method
Nerve Net - physiology
Neuronal Plasticity - physiology
Normal Distribution
Original Article
Probability
Statistics, Nonparametric
Synapses - physiology
Synaptic Transmission - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Signal processing in the cerebral cortex is thought to involve a common multi-purpose algorithm embodied in a canonical cortical micro-circuit that is replicated many times over both within and across cortical regions. Operation of this algorithm produces widely distributed but coherent and relevant patterns of activity. The theory of Coherent Infomax provides a formal specification of the objectives of such an algorithm. It also formally derives specifications for both the short-term processing dynamics and for the learning rules whereby the connection strengths between units in the network can be adapted to the environment in which the system finds itself. A central assumption of the theory is that the local processors can combine reliable signal coding with flexible use of those codes because they have two classes of synaptic connection: driving connections which specify the information content of the neural signals, and contextual connections which modulate that signal processing. Here, we make the biological relevance of this theory more explicit by putting more emphasis upon the contextual guidance of ongoing processing, by showing that Coherent Infomax is consistent with a particular Bayesian interpretation for the contextual guidance of learning and processing, by explicitly specifying rules for on-line learning, and by suggesting approximations by which the learning rules can be made computationally feasible within systems composed of very many local processors.
ISSN:0092-8240
1522-9602
DOI:10.1007/s11538-010-9564-x