Analytical integrate-and-fire neuron models with
conductance-based dynamics and realistic
postsynaptic potential time course for
event-driven simulation strategies.
Michelle Rudolph-Lilith, Mathieu Dubois and Alain Destexhe
Neural Computation 24: 1426-1461, 2012.
Abstract
In a previous paper (Rudolph & Destexhe,
2006), we proposed various models, the gIF neuron models, of
analytical integrate-and-fire (IF) neurons with conductance- based
(COBA) dynamics for use in event-driven simulations. These models
are based on an analytical approximation of the differential
equation describing the IF neuron with exponential synaptic
conductances, and were successfully tested with respect to their
response to random and oscillating inputs. Because they are
analytical and mathematically simple, the gIF models are best
suited for fast event-driven simulation strategies. However, the
drawback of such models is they rely on a non-realistic
postsynaptic potential (PSP) time course, consisting of a
discontinuous jump followed by a decay governed by the membrane
time constant. Here, we address this limitation by conceiving an
analytical approximation of the COBA IF neuron model with the
full PSP time course. The subthreshold and suprathreshold response
of this gIF4 model reproduces remarkably well the postsynaptic
responses of the numerically solved passive membrane equation
subject to conductance noise, while gaining at least two orders of
magnitude in computational performance. Although the analytical
structure of the gIF4 model is more complex than that of its
predecessors due to the necessity of calculating future spike times,
a simple and fast algorithmic implementation for use in
large-scale neural network simulations is proposed.
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