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Neural Computation, Vol 8, 1643-1652, Copyright © 1996 by The MIT Press

ARTICLES

Response characteristics of a low-dimensional model neuron

B Cartling
Department of Theoretical Physics, Royal Institute of Technology, Stockholm, Sweden.

It is shown that a low-dimensional model neuron with a response time constant smaller than the membrane time constant closely reproduces the activity and excitability behavior of a detailed conductance-based model of Hodgkin-Huxley type. The fast response of the activity variable also makes it possible to reduce the model to a one- dimensional model, in particular for typical conditions. As an example, the reduction to a single-variable model from a multivariable conductance-based model of a neocortical pyramidal cell with somatic input is demonstrated. The conditions for avoiding a spurious damped oscillatory response to a constant input are derived, and it is shown that a limit-cycle response cannot occur. The capability of the low- dimensional model to approximate higher-dimensional models accurately makes it useful for describing complex dynamics of nets of interconnected neurons. The simplicity of the model facilitates analytic studies, elucidations of neurocomputational mechanisms, and applications to large-scale systems.


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P. A. Fortier, E. Guigon, and Y. Burnod
Supervised Learning in a Recurrent Network of Rate-Model Neurons Exhibiting Frequency Adaptation
Neural Comput., September 1, 2005; 17(9): 2060 - 2076.
[Abstract] [Full Text] [PDF]


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Copyright © 1996 by The MIT Press.