Dynamic updating of distributed neural representations using forward models.

Abstract:

:In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527-533, 2003; Oztop et al. in Neural Netw 19(3):254-271, 2006; Wolpert et al. in Philos Trans R Soc 358:593-602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brain.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Sauser EL,Billard AG

doi

10.1007/s00422-006-0131-3

subject

Has Abstract

pub_date

2006-12-01 00:00:00

pages

567-88

issue

6

eissn

0340-1200

issn

1432-0770

journal_volume

95

pub_type

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