Document Detail


Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation.
MedLine Citation:
PMID:  22131133     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The hippocampus is a brain structure critical for memory functioning. Its network dynamics include several patterns such as sharp waves that are generated in the CA3 region. To understand how population outputs are generated, models need to consider aspects of network size, cellular and synaptic characteristics and context, which are necessarily 'balanced' in appropriate ways to produce particular outputs. Thick slice hippocampal preparations spontaneously produce sharp waves that are initiated in CA3 regions and depend on the right balance of glutamatergic activities. As a step toward developing network models that can explain important balances in the generation of hippocampal output, we develop models of CA3 pyramidal cells. Our models are single compartment in nature, use an Izhikevich-type structure and involve parameter values that are specifically designed to encompass CA3 intrinsic properties. Importantly, they incorporate spike frequency adaptation characteristics that are directly comparable to those measured experimentally. Excitatory networks using these model cells are able to produce bursting suggesting that the amount of spike frequency adaptation expressed in the biological cells is an essential contributor to network bursting, and as such, may be important for sharp wave generation. The network bursting mechanism is numerically dissected showing the critical balance between adaptation and excitatory drive. The compact nature of our models allows large network simulations to be efficiently computed. This, together with the linkage of our models to cellular characteristics, will allow us to develop an understanding of population output of CA3 hippocampus with direct biological comparisons.
Authors:
Muhammad Dur-E-Ahmad; Wilten Nicola; Sue Ann Campbell; Frances K Skinner
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-12-2
Journal Detail:
Title:  Journal of computational neuroscience     Volume:  -     ISSN:  1573-6873     ISO Abbreviation:  -     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-12-1     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9439510     Medline TA:  J Comput Neurosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
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