Conflict resolution and learning probability matching in a neural cell-assembly architecture
Article
Belavkin, R. and Huyck, C. 2011. Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research. 12 (2), pp. 93-101. https://doi.org/10.1016/j.cogsys.2010.08.003
| Type | Article |
|---|---|
| Title | Conflict resolution and learning probability matching in a neural cell-assembly architecture |
| Authors | Belavkin, R. and Huyck, C. |
| Abstract | Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in the human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved. We describe an earlier proposed architecture implementing an adaptive conflict resolution process between CAs, and then evaluate it by modelling the probability matching phenomenon in a classic two-choice task. The model and its results are discussed in view of mathematical theory of learning and existing cognitive architectures. |
| Keywords | Artificial intelligence; Cognitive science; Neuroscience; Decision making; Intelligent agents; Learning; Bayesian modelling; Computational neuroscience; Human experimentation |
| Research Group | Artificial Intelligence group |
| Language | English |
| Publisher | Elsevier |
| Journal | Cognitive Systems Research |
| ISSN | 1389-0417 |
| Publication dates | |
| 30 Jun 2011 | |
| Online | 26 Aug 2010 |
| Publication process dates | |
| Deposited | 27 Jan 2011 |
| Output status | Published |
| Accepted author manuscript | License |
| Copyright Statement | Post refereed version as permitted by publisher. |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cogsys.2010.08.003 |
| Scopus EID | 2-s2.0-79952196078 |
| Web of Science identifier | WOS:000288008100003 |
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