In:
Neural Computation, MIT Press, Vol. 16, No. 10 ( 2004-10-01), p. 2101-2124
Abstract:
Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firing-rate adaptation, two ubiquitous features in the central nervous system that have been previously overlooked in constructing rate models. The procedure is general and applies to any model of firing unit. As examples, we apply it to the leaky integrate-and-fire (IF) neuron, the leaky IF neuron with reversal potentials, and to the quadratic IF neuron. Two mechanisms of adaptation are considered, one due to an after hyperpolarization current and the other to an adapting threshold for spike emission. The parameters of these simple models can be tuned to match experimental data obtained from neocortical pyramidal neurons. Finally, we show how the stationary model can be used to predict the time-varying activity of a large population of adapting neurons.
Type of Medium:
Online Resource
ISSN:
0899-7667
,
1530-888X
DOI:
10.1162/0899766041732468
Language:
English
Publisher:
MIT Press
Publication Date:
2004
detail.hit.zdb_id:
1025692-1
detail.hit.zdb_id:
1498403-9
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