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| Research article summary (published 30 Dec 2002): |
Comparison of computational models of familiarity discrimination in the perirhinal cortex.
Full Abstract
This study compares the efficiency and plausibility of published computational models of familiarity discrimination in the perirhinal cortex. Substantial evidence indicates that the perirhinal cortex is involved in both the familiarity discrimination aspect of recognition memory and in perceptual functions involved with representations of complete stimuli (i.e., object identification). Published models of how the perirhinal cortex may perform familiarity discrimination can be divided into two groups. The first group assumes that a proportion of perirhinal neurons form a network specialised just for familiarity discrimination (these models may be based on Hebbian or anti-Hebbian synaptic plasticity). In contrast, the second group assumes that both familiarity discrimination and learning representations of complete stimuli are performed within a single combined network. This study establishes that when the responses of neurons that provide input to the familiarity discrimination network are correlated (as indicated by experimental data), specialised networks based on anti-Hebbian learning may recognise the previous occurrence of many more stimuli (i.e., have a capacity up to thousands of times larger) than specialised networks based on Hebbian learning. The currently published combined models do not learn an optimal stimulus representation (they do not fully extract statistically independent features), and hence their capacities are even lower than those of the specialised models based on Hebbian learning. Hence, the combined models published thus far are critically less efficient than the specialised models based on anti-Hebbian learning. This study also compares the consistency of the models with experimental observations concerning what is known of synaptic plasticity in the perirhinal cortex and the responses of its neurons. Many theoretically important parameters remain undetermined, and experiments are suggested to provide information critical for refining and distinguishing between the various models. However, the above theoretical arguments and currently published data favour the existence of a separate network specialised for familiarity discrimination.
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Author information
Author/s: Bogacz, Rafal (R); Brown, Malcolm W (MW);
Affiliation: Department of Computer Science, University of Bristol, Bristol, United Kingdom.
Journal and publication information
Publication Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Review
Journal: Hippocampus (Hippocampus), published in United States. (Language: eng)
Reference: 2003-; vol 13 (issue 4) : pp 494-524
Dates: Created 2003/07/02; Completed 2003/08/13; Revised 2006/11/15;
PMID: 12836918, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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