Effective minimal threshold models of neuronal activity

Abstract

This work investigates the capacity of Integrate-and-Fire-type (IF) models to quantitatively predict spike trains of real neurons in various laboratory and in vivo-like settings. A step-by-step methodology is developed to build an equivalent effective model of the threshold type, namely the Spike Response Model, from intracellular recordings. The methodology provides a simple and flexible framework and allows straightforward estimation of model parameters, all of which except the threshold are directly extracted from sample recordings without approximations. Additional features like spike-frequency adaptation, simulated synaptic conductances (as opposed to direct current injection) or stochastic spike initiation can be easily included in a second step. The methodology is tested with a simulated detailed Hodgkin-Huxley-type model of an interneuron as well as with layer 5 neocortical pyramidal neurons. The effective model performs well in predicting the spike train of cortical neurons. We show that the timing of spikes can be predicted with great accuracy as well as other typical measures of neuronal activity like the average discharge rate and the interspike interval distribution. To some extent, even the subthreshold voltage between spikes is correctly predicted. The ability of the effective model to predict spikes of layer 5 pyramidal neurons with a correct timing is closely related to the input characteristics. In this sense, the model follows the same lines as real neurons when evaluating their input-dependent ability to repetitively and reliably produce the same output spike train given the same input. IF models of neuronal activity are commonly thought to be too simple to account for the rich firing behavior of real neurons. This work suggests that, at least in the considered settings, the picture of a neuron that combines linear summation with a threshold criterion is not too wrong and provides a justification to the use of IF models in large scale network simulations.