A statistical model for in vivo neuronal dynamics

TL;DR

This paper introduces a new statistical model for in vivo neuronal recordings that captures complex neuronal dynamics by combining Gaussian processes with nonlinear spike intensity functions, enabling precise data characterization.

Contribution

The paper presents a novel stochastic neuron model that effectively characterizes in vivo data, capturing diverse dynamical features and allowing efficient fitting without overfitting.

Findings

Model captures arbitrary subthreshold autocovariance functions

Model can fit in vivo data efficiently

Enables comparison of neuronal recordings across conditions

Abstract

Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. Finally, we show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.