TY - JOUR T1 - From Spiking Neuron Models to Linear-Nonlinear Models A1 - Ostojic, Srdjan A1 - Brunel, Nicolas Y1 - 2011/01/20 N2 - Author Summary Deciphering the encoding of information in the brain implies understanding how individual neurons emit action potentials (APs) in response to time-varying stimuli. This task is made difficult by two facts: (i) although the biophysics of AP generation are well understood, the dynamics of the membrane potential in response to a time-varying input are highly complex; (ii) the firing of APs in response to a given stimulus is inherently stochastic as only a fraction of the inputs to a neuron are directly controlled by the stimulus, the remaining being due to the fluctuating activity of the surrounding network. As a result, the input-output transform of individual neurons is often represented with the help of simplified phenomenological models that do not take into account the biophysical details. In this study, we directly relate a class of such phenomenological models, the so called linear-nonlinear models, with more biophysically detailed spiking neuron models. We provide a quantitative mapping between the two classes of models, and show that the linear-nonlinear models provide a good approximation of the input-output transform of spiking neurons, as long as the fluctuating inputs from the surrounding network are not exceedingly weak. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 1 UR - https://doi.org/10.1371/journal.pcbi.1001056 SP - e1001056 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1001056 ER -