An Evolutionary Autonomous Agent with Visual Cortex and Recurrent Spiking Columnar Neural Network

Spiking neural networks are computationally more power- ful than conventional artificial neural networks (1). Although this fact should make them especially desirable for use in evolutionary autono- mous agent research, several factors have limited their application. This work demonstrates an evolutionary agent with a sizeable recurrent spi- king neural network containing a biologically motivated columnar visual cortex. This model is instantiated in spiking neural network simulation software and challenged with a dynamic image recognition and memory task. We use a genetic algorithm to evolve generations of this brain mo- del that instinctively perform progressively better on the task. This early work builds a foundation for determining which features of biological neural networks are important for evolving capable dynamic cognitive agents.

[1]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[2]  D. Hubel,et al.  Ferrier lecture - Functional architecture of macaque monkey visual cortex , 1977, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  T Bonhoeffer,et al.  Orientation selectivity in pinwheel centers in cat striate cortex. , 1997, Science.

[4]  Henry Markram,et al.  An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing , 2001, Neural Computation.

[5]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[6]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Desimone,et al.  Neural Mechanisms of Visual Working Memory in Prefrontal Cortex of the Macaque , 1996, The Journal of Neuroscience.

[8]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[9]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[10]  Vasant Honavar,et al.  Evolutionary Design of Neural Architectures -- A Preliminary Taxonomy and Guide to Literature , 1995 .

[11]  D. Sholl.  The organization of the cerebral cortex , 1957 .

[12]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

[13]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[14]  Frederick C. Harris,et al.  Implementation of a Biologically Realistic Parallel Neocortical-Neural Network Simulator , 2001, PPSC.

[15]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[17]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[18]  E. Callaway.  Local circuits in primary visual cortex of the macaque monkey. , 1998, Annual review of neuroscience.

[19]  E. Ruppin.  Evolutionary autonomous agents: A neuroscience perspective , 2002, Nature Reviews Neuroscience.

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

[21]  V. Mountcastle.  The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.