Conflict resolution and learning probability matching in a neural cell-assembly architecture.

Belavkin, Roman V. and Huyck, Christian R. (2011) Conflict resolution and learning probability matching in a neural cell-assembly architecture. Cognitive Systems Research, 12 (2). pp. 93-101. ISSN 1389-0417

[img]
Preview
PDF
183kB

Official URL: http://www.sciencedirect.com/science/article/B6W6C...

This item is available in the Library Catalogue

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.

Item Type:Article
Research Areas:School of Science and Technology > Computer Science
School of Science and Technology > Computer Science > Artificial Intelligence group
Citations on ISI Web of Science:0
ID Code:6935
Permissions granted by publisher:Post refereed version as permitted by publisher.
Useful Links:
Deposited On:27 Jan 2011 10:35
Last Modified:11 Oct 2014 06:34

Repository staff only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year