Emergence of Rules in Cell Assemblies of fLIF Neurons

Inspired by biological cognition, CABOT project explores the ways symbolic processing can emerge in a system of neural cell assemblies (CAs). Here we show how a stochastic meta--control process can regulate learning of associations between the CAs, the neural basis of symbols. An experiment illustrates the learning between CAs representing conditions actions pairs, which leads to CA--based representations of 'if--then' rules.

[1]  Yoshio Sakurai,et al.  The search for cell assemblies in the working brain , 1998, Behavioural Brain Research.

[2]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[3]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[4]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.

[5]  P. Anandan,et al.  Pattern-recognizing stochastic learning automata , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Roman V. Belavkin,et al.  On emotion, learning and uncertainty : a cognitive modelling approach , 2003 .

[7]  Richard Granger,et al.  Engines of the Brain: The Computational Instruction Set of Human Cognition , 2006, AI Mag..

[8]  Abraham Wald,et al.  Statistical Decision Functions , 1951 .

[9]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[10]  Richard Reviewer-Granger Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[11]  Frank E. Ritter The Use of Entropy for Analysis and Control of Cognitive Models , 2003 .

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[14]  Christian R. Huyck,et al.  Counting with neurons: rule application with nets of fatiguing leaking integrate and fire neurons. , 2006 .

[15]  Eric Chown,et al.  Tracing Recurrent Activity in Cognitive Elements (TRACE): a Model of Temporal Dynamics in a Cell Assembly , 1991 .

[16]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[17]  P. Dayan,et al.  Cortical substrates for exploratory decisions in humans , 2006, Nature.

[18]  Roman V. Belavkin,et al.  Acting Irrationally to Improve Performance in Stochastic Worlds , 2005, SGAI Conf..

[19]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[20]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[21]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[22]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[23]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[24]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .