Connectionists: Feedback in neural circuits (OR: How to expand liquid computing)

[Wolfgang Maass]

The paper

Computational Aspects of Feedback in Neural Circuits

                        by
Wolfgang Maass,  Prashant Joshi , and Eduardo Sontag

is now available from the homepages of the authors.

There will be a talk and poster on it at NIPS 2005
(under the title "Principles of real-time computing with feedback 
applied to cortical microcircuit models").

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 large 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.


-- 
Wolfgang Maass
Professor of Computer Science
Technische Universitaet Graz
http://www.igi.tugraz.at/maass/