A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input

Biol Cybern. 2006 Jul;95(1):1-19. doi: 10.1007/s00422-006-0068-6. Epub 2006 Apr 19.


The integrate-and-fire neuron model is one of the most widely used models for analyzing the behavior of neural systems. It describes the membrane potential of a neuron in terms of the synaptic inputs and the injected current that it receives. An action potential (spike) is generated when the membrane potential reaches a threshold, but the actual changes associated with the membrane voltage and conductances driving the action potential do not form part of the model. The synaptic inputs to the neuron are considered to be stochastic and are described as a temporally homogeneous Poisson process. Methods and results for both current synapses and conductance synapses are examined in the diffusion approximation, where the individual contributions to the postsynaptic potential are small. The focus of this review is upon the mathematical techniques that give the time distribution of output spikes, namely stochastic differential equations and the Fokker-Planck equation. The integrate-and-fire neuron model has become established as a canonical model for the description of spiking neurons because it is capable of being analyzed mathematically while at the same time being sufficiently complex to capture many of the essential features of neural processing. A number of variations of the model are discussed, together with the relationship with the Hodgkin-Huxley neuron model and the comparison with electrophysiological data. A brief overview is given of two issues in neural information processing that the integrate-and-fire neuron model has contributed to - the irregular nature of spiking in cortical neurons and neural gain modulation.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Action Potentials / physiology*
  • Animals
  • Central Nervous System / physiology*
  • Humans
  • Models, Neurological*
  • Neural Pathways / physiology*
  • Neurons / physiology*
  • Stochastic Processes
  • Synapses / physiology*
  • Synaptic Membranes / physiology
  • Synaptic Transmission / physiology