Neural Computation, Vol 7, 713-734, Copyright © 1995 by The MIT Press
Synapse models for neural networks: from ion channel kinetics to multiplicative coefficient wij
F Chapeau-Blondeau and N Chambet
Institut de Biologie Theorique, Faculte des Sciences, Universite d'Angers, France.
This paper relates different levels at which the modeling of synaptic
transmission can be grounded in neural networks: the level of ion channel
kinetics, the level of synaptic conductance dynamics, and the level of a
scalar synaptic coefficient. The important assumptions to reduce a synapse
model from one level to the next are explicitly exhibited. This coherent
progression provides control on what is discarded and what is retained in
the modeling process, and is useful to appreciate the significance and
limitations of the resulting neural networks. This methodic simplification
terminates with a scalar synaptic efficacy as it is very often used in
neural networks, but here its conditions of validity are explicitly
displayed. This scalar synapse also comes with an expression that directly
relates it to basic quantities of synaptic functioning, and it can be
endowed with meaningful physical units and realistic numerical values. In
addition, it is shown that the scalar synapse does not receive the same
expression in neural networks operating with spikes or with firing rates.
These coherent modeling elements can help to improve, adjust, and refine
the investigation of neural systems and their remarkable collective
properties for information processing.
