A Model for Real-Time Computation in Generic Neural Microcircuits

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.

[1]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[2]  H. Markram,et al.  Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. , 2000, Science.

[3]  J J Hopfield,et al.  What is a moment? "Cortical" sensory integration over a brief interval. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Henry Markram,et al.  A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models , 2002, Biologically Motivated Computer Vision.

[5]  M M Merzenich,et al.  Temporal information transformed into a spatial code by a neural network with realistic properties , 1995, Science.

[6]  Henry Markram,et al.  Computational models for generic cortical microcircuits , 2004 .

[7]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[8]  J J Hopfield,et al.  What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[9]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Eduardo D. Sontag,et al.  Neural Systems as Nonlinear Filters , 2000, Neural Computation.