Exact Solution of the Nonlinear Dynamics of Recurrent Neural Mechanisms for Direction Selectivity


Autoria(s): Giese, M.A.; Xie, X.
Data(s)

20/10/2004

20/10/2004

01/08/2002

Resumo

Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions ("lurching activity waves") that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.

Formato

7 p.

2554351 bytes

1165357 bytes

application/postscript

application/pdf

Identificador

AIM-2002-013

CBCL-220

http://hdl.handle.net/1721.1/7273

Idioma(s)

en_US

Relação

AIM-2002-013

CBCL-220

Palavras-Chave #AI #direction #visual cortex #nonlinear dynamics #lurching waves #stability #recurre