A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.

Conference paper


Belavkin, R. and Huyck, C. 2009. A model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies. 9th International conference on cognitive modelling {ICCM 2009]. University of Manchester 24 - 26 Jul 2009
TypeConference paper
TitleA model of probability matching in a two-choice task based on stochastic control of learning in neural cell-assemblies.
AuthorsBelavkin, 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 \cite{Belavkin08:_ecai08} implementing such a process, 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 as well as some hypotheses about neural functioning in the brain.

Research GroupArtificial Intelligence group
Conference9th International conference on cognitive modelling {ICCM 2009]
Publication dates
PrintJul 2009
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Deposited24 Mar 2010
Output statusPublished
Web address (URL)http://www.eis.mdx.ac.uk/staffpages/rvb/publications/rvb-crh-iccm09.pdf
LanguageEnglish
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Belavkin, R. 2008. The duality of utility and information in optimally learning systems. 7th IEEE International Conference on Cybernetic Intelligent Systems. London, UK 09 - 10 Sep 2008 London IEEE. https://doi.org/10.1109/UKRICIS.2008.4798960
Do neural models scale up to a human brain?
Belavkin, R. 2007. Do neural models scale up to a human brain? International Joint Conference on Neural Networks (IJCNN 2007). Orlando, Florida 12 - 17 Aug 2007 IEEE. pp. 2312-2317 https://doi.org/10.1109/IJCNN.2007.4371319
Creating hierarchical categories using cell assemblies
Huyck, C. 2007. Creating hierarchical categories using cell assemblies. Connection Science. 19 (1), pp. 1-24. https://doi.org/10.1080/09540090600779713
Relevance feedback and cross-language information retrieval
Orengo, V. and Huyck, C. 2006. Relevance feedback and cross-language information retrieval. Information Processing and Management. 42 (5), pp. 1203-1217. https://doi.org/10.1016/j.ipm.2005.12.003
Information retrieval and categorisation using a cell assembly network
Huyck, C. and Orengo, V. 2005. Information retrieval and categorisation using a cell assembly network. Neural Computing and Applications. 14 (4), pp. 282-289. https://doi.org/10.1007/s00521-004-0464-6
Acting irrationally to improve performance in stochastic worlds
Belavkin, R. 2005. Acting irrationally to improve performance in stochastic worlds. Bramer, M., Coenen, F. and Allen, T. (ed.) 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK 2005 Springer. pp. 305-316 https://doi.org/10.1007/978-1-84628-226-3_23
Overlapping cell assemblies from correlators
Huyck, C. 2004. Overlapping cell assemblies from correlators. Neural Computing Letters. 56, pp. 435-439. https://doi.org/10.1016/j.neucom.2003.08.003