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 Cybernjournal_title
Biological cyberneticsauthors
Sauser EL,Billard AGdoi
10.1007/s00422-006-0131-3subject
Has Abstractpub_date
2006-12-01 00:00:00pages
567-88issue
6eissn
0340-1200issn
1432-0770journal_volume
95pub_type
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