Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.
We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70-80 percent of the spikes in the full model (with temporal precision of +/-2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.
Laboratory of Computational Neuroscience, Swiss Federal Institute of Technology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. renaud.jolivet@epfl.ch
Renaud
R
Jolivet
Timothy J
TJ
Lewis
Wulfram
W
Gerstner
D000200:Action Potentials; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D004056:Differential Threshold; D004553:Electric Conductivity; D004558:Electric Stimulation; D006801:Humans; D008564:Membrane Potentials; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D017729:Presynaptic Terminals / Q000502:physiology; D013269:Stochastic Processes; D013997:Time Factors