PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Computational aspects of feedback in neural circuits
Wolfgang Maass, Prashant Joshi and E. D. Sontag
PLOS Computational Biology Volume in press, 2006.

Abstract

It had previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate in this article the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise the resulting computational model can perform a large class of biologically relevant real-time computations that require a non-fading memory. We demonstrate these computational implications of feedback both theoretically and through computer simulations of detailed cortical microcircuit models. We show that the application of simple learning procedures (such as linear regression or perceptron learning) enables such circuits, in spite of their complex inherent dynamics, to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as for example genetic regulatory networks.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2646
Deposited By:Prashant Joshi
Deposited On:22 November 2006