HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Wu, S. Articles by Amari, S.-i. Search for Related Content PubMed PubMed Citation Articles by Wu, S. Articles by Amari, S.-i.

Computing with Continuous Attractors: Stability and Online Aspects

siwu{at}sussex.ac.uk, Department of Informatics, University of Sussex, Brighton, U.K.

Shun-ichi Amari

amari{at}brain.riken.go.jp, Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Tokyo, Japan

Two issues concerning the application of continuous attractors in neural systems are investigated: the computational robustness of continuous attractors with respect to input noises and the implementation of Bayesian online decoding. In a perfect mathematical model for continuous attractors, decoding results for stimuli are highly sensitive to input noises, and this sensitivity is the inevitable consequence of the system's neutral stability. To overcome this shortcoming, we modify the conventional network model by including extra dynamical interactions between neurons. These interactions vary according to the biologically plausible Hebbian learning rule and have the computational role of memorizing and propagating stimulus information accumulated with time. As a result, the new network model responds to the history of external inputs over a period of time, and hence becomes insensitive to short-term fluctuations. Also, since dynamical interactions provide a mechanism to convey the prior knowledge of stimulus, that is, the information of the stimulus presented previously, the network effectively implements online Bayesian inference. This study also reveals some interesting behavior in neural population coding, such as the trade-off between decoding stability and the speed of tracking time-varying stimuli, and the relationship between neural tuning width and the tracking speed.

This article has been cited by other articles:

				S. Wu, K. Hamaguchi, and S.-i. Amari Dynamics and Computation of Continuous Attractors Neural Comput., April 1, 2007; 20(4): 994 - 1025. [Abstract] [Full Text] [PDF]