Firing-rate response of linear and nonlinear integrate-and-fire neurons to modulated current-based and conductance-based synaptic drive

Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Aug;76(2 Pt 1):021919. doi: 10.1103/PhysRevE.76.021919. Epub 2007 Aug 20.


Integrate-and-fire models are mainstays of the study of single-neuron response properties and emergent states of recurrent networks of spiking neurons. They also provide an analytical base for perturbative approaches that treat important biological details, such as synaptic filtering, synaptic conductance increase, and voltage-activated currents. Steady-state firing rates of both linear and nonlinear integrate-and-fire models, receiving fluctuating synaptic drive, can be calculated from the time-independent Fokker-Planck equation. The dynamic firing-rate response is less easy to extract, even at the first-order level of a weak modulation of the model parameters, but is an important determinant of neuronal response and network stability. For the linear integrate-and-fire model the response to modulations of current-based synaptic drive can be written in terms of hypergeometric functions. For the nonlinear exponential and quadratic models no such analytical forms for the response are available. Here it is demonstrated that a rather simple numerical method can be used to obtain the steady-state and dynamic response for both linear and nonlinear models to parameter modulation in the presence of current-based or conductance-based synaptic fluctuations. To complement the full numerical solution, generalized analytical forms for the high-frequency response are provided. A special case is also identified--time-constant modulation--for which the response to an arbitrarily strong modulation can be calculated exactly.

MeSH terms

  • Action Potentials
  • Animals
  • Humans
  • Models, Biological
  • Models, Neurological
  • Models, Statistical
  • Models, Theoretical
  • Neural Pathways
  • Neurons / metabolism*
  • Neurons / physiology*
  • Probability
  • Synapses / metabolism
  • Synaptic Transmission
  • Time Factors