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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shah, N. Alexandre, F. |
| Copyright Year | 2011 |
| Description | Author affiliation: French National Institute in Computer Science and Control (INRIA), Centre of Research INRIA of Nancy, 615 rue du Jardin Botanique, CS 20101, F-54603 Villers-les-Nancy, France (Alexandre, F.) || Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), associated to the CNRS and the universities of Nancy, France (Shah, N.) |
| Abstract | Basal Ganglia, a group of sub-cortical neuronal nuclei in the brain, are commonly described as the neuronal substratum to Reinforcement Learning. Since the seminal work by Schultz [1], a huge amount of work has been done to deepen that analogy, from functional and anatomic points of view. Nevertheless, a noteworthy architectural hint has been hardly explored: the outstanding reduction of dimensionality from the input to the output of the basal ganglia. Bar-Gad et al. [2] have suggested that this transformation could correspond to a Principal Component Analysis but did not explore the full functional consequences of this hypothesis. In this paper, we propose to study this mechanism within a model more realistic from a computational neuroscience point of view. Particularly, we show its feasibility when the loop is closed, in the framework of Action Selection. |
| Starting Page | 836 |
| Ending Page | 842 |
| File Size | 462721 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033308 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Principal component analysis Learning Brain modeling Biological system modeling Mathematical model Basal ganglia |
| Content Type | Text |
| Resource Type | Article |
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