A computational model of perirhinal cortex: Gating and repair of input to the hippocampus

Adam W. Lester, Michael D. Howard, Jean Marc Fellous, Rajan Bhattacharyya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The medial temporal lobe-which includes the hippocampus, as well as perirhinal, parahippocampal, and entorhinal cortices-is required for declarative memory. We focus on the role of the perirhinal cortex (PRC) in relaying semantic representations from temporal cortex to the ventral hippocampus. It has been argued that the PRC is more than a simple relay. We review evidence that the PRC, in conjunction with the entorhinal cortex, serves to both gate information transfer to the hippocampus in response to an externally generated signal, and to improve the fidelity of this input prior to its mnemonic processing within the hippocampus. We present the first explicit model of externally mediated PRC gating based on several gating mechanisms previously modeled for generic cortical regions; we discuss the merits of our model with respect these existing theoretical gating mechanisms, and also outline three possible external control signals and their functional implications. We constructed a biologically plausible neural network model (InhibGate) based on the available literature, and compared it to a slightly adapted three layer network as a qualitative standard of comparison. In nearly every condition, the InhibGate network was more effective at information gating and pattern repair than the comparison three layer network. Our experiments support the proposal that inhibition within the rhinal cortices can block or admit information transfer to the hippocampus in response to an externally supplied excitatory signal. In addition, our experiments reveal a possible role for the rhinal cortices in repairing noisy or incomplete data: a role that has been previously ascribed to the hippocampus.

Original languageEnglish (US)
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
DOIs
StatePublished - Dec 1 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: Aug 4 2013Aug 9 2013

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/TerritoryUnited States
CityDallas, TX
Period8/4/138/9/13

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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