Characterizing spiking in noisy type II neurons. (21st January 2015) - Bod׳ová, Katarína; Paydarfar, David; Forger, Daniel B.
Abstract: Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin–Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model. Abstract : Highlights: The effect of channel stochasticity and input variability on a neuron is studied. A probabilistic switching model is proposed to capture the neuronal firing. The model explains firing rate statistics besides the mean firing rate. The model is easily applicable to experimental recordings.