Abstract

   This paper outlines a dynamic theory of development and adaptation in
   neural networks with feedback connections.  Given input ensemble, the
   connections change in strength according  to  an associative learning
   rule and approach  a  stable  state  where  the  neuronal outputs are
   decorrelated.  We apply  this  theory  to  primary  visual cortex and
   examine  the  implications  of  the  dynamical  decorrelation of  the
   activities  of  orientation  selective  cells  by  the  intracortical
   connections.  The theory gives a unified and quantitative explanation
   of  the  psychophysical  experiments  on   orientation  contrast  and
   orientation adaptation.  Using only  one  parameter,  we achieve good
   agreements between the theoretical  predictions  and the experimental
   data.

   (In: Advances in Neural Information Processing Systems 7, Tesauro G,
    Touretzky DS, Leen TK, eds) MIT Press, Cambridge, MA, page 925-932. 1995)

   (Papers' Index of Dawei Dong)