A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

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dc.contributor.authorFaghihi, Faramarz
dc.contributor.authorMoustafa, Ahmed A.
dc.date.accessioned2015-09-10T18:12:02Z
dc.date.available2015-09-10T18:12:02Z
dc.date.issued2015-03-25
dc.description.abstractInformation processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed.
dc.description.sponsorshipThe research is supported by NSF grant number IIS-1302256. Publication of this article was funded in part by George Mason University Libraries Open Access Publishing Fund.
dc.identifier.citationFaghihi F and Moustafa AA (2015) A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia. Front. Syst. Neurosci. 9:42. doi: 10.3389/fnsys.2015.00042
dc.identifier.doihttp://dx.doi.org/10.3389/fnsys.2015.00042
dc.identifier.urihttps://hdl.handle.net/1920/9833
dc.language.isoen_US
dc.publisherFrontiers Media
dc.rightsAttribution 3.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/
dc.subjectPattern separation
dc.subjectSparse spiking
dc.subjectSparse coding
dc.subjectFeedback inhibition
dc.subjectConnectivity rate
dc.subjectMutual information
dc.subjectDentate gyrus
dc.subjectEntorhinal cortex
dc.titleA computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia
dc.typeArticle

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