Document Detail


A population density approach that facilitates large-scale modeling of neural networks: extension to slow inhibitory synapses.
MedLine Citation:
PMID:  11244554     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we calculate the evolution of a probability density function (PDF), which describes the distribution of neurons over state-space. The population firing rate is given by the flux of probability across the threshold voltage for firing an action potential. In the case of fast synaptic conductances, the PDF was one-dimensional, as the state of a neuron was completely determined by its transmembrane voltage. An exact extension to slow inhibitory synapses increases the dimension of the PDF to two or three, as the state of a neuron now includes the state of its inhibitory synaptic conductance. However, by assuming that the expected value of a neuron's inhibitory conductance is independent of its voltage, we derive a reduction to a one-dimensional PDF and avoid increasing the computational complexity of the problem. We demonstrate that although this assumption is not strictly valid, the results of the reduced model are surprisingly accurate.
Authors:
D Q Nykamp; D Tranchina
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Neural computation     Volume:  13     ISSN:  0899-7667     ISO Abbreviation:  Neural Comput     Publication Date:  2001 Mar 
Date Detail:
Created Date:  2001-03-13     Completed Date:  2001-05-03     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  511-46     Citation Subset:  IM    
Affiliation:
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.
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MeSH Terms
Descriptor/Qualifier:
Animals
Electric Conductivity
Excitatory Postsynaptic Potentials / physiology
Models, Neurological*
Neural Inhibition / physiology*
Neural Networks (Computer)*
Neurons / physiology
Synapses / physiology*
Visual Cortex / cytology,  physiology

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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