## Integrate-and-fire neurons with high-conductance state dynamics for
event-driven simulation strategies.

#### Michelle Rudolph and Alain Destexhe

* Neural Computation* 18: 2146-2210, 2006.

## Abstract

Event-driven simulation strategies were proposed recently to simulate
integrate- and-fire (IF) type neuronal models. These strategies can lead to
computationally efficient algorithms for simulating large-scale networks of
neurons, but most importantly, such approaches are more precise than
traditional clock-driven numerical integration approaches because the timing
of spikes is treated exactly. The drawback of such event-driven methods is
that, in order to be efficient, the membrane equations must be solvable
analytically, or at least provide simple analytic approximations for the state
variables describing the system. This requirement prevents, in general, the
use of conductance-based synaptic interactions within the framework of
event-driven simulations and, thus, the investigation of network paradigms
where synaptic conductances are important. We propose here a number of
extensions of the classical leaky IF neuron model involving approximations of
the membrane equation with conductance-based synaptic current, which lead to
simple analytic expressions for the membrane state which, therefore, can be
used in the event-driven framework. These conductance-based IF models (gIF
models) are compared to commonly used models, such as the leaky IF model or
biophysical models in which conductances are explicitly integrated. All
models are compared with respect to various spiking response properties in the
presence of synaptic activity, such as the spontaneous discharge statistics,
the temporal precision in resolving synaptic inputs, and gain modulation under
in vivo-like synaptic bombardment. Being based on the passive membrane
equation with fixed-threshold spike generation, the proposed gIF models are
situated in between leaky IF and biophysical models, but are much closer to
the latter with respect to their dynamic behavior and response
characteristics, while still being nearly as computationally efficient as
simple IF neuron models. gIF models should, therefore, provide a useful tool
for efficient and precise simulation of large-scale neuronal networks with
realistic, conductance-based synaptic interactions.

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