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A Statistical Model for In Vivo Neuronal Dynamics.

Simone Carlo Surace^1,2, Jean-Pascal Pfister²

¹Department of Physiology, University of Bern, Bern, Switzerland.

|November 17, 2015

Summary

This study introduces a new single neuron model for analyzing in vivo recordings by using a stochastic process. This model accurately captures neuron dynamics and facilitates comparisons across different experimental conditions.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Biophysics

Background:

Traditional single neuron models (e.g., Hodgkin-Huxley, integrate-and-fire) are limited for in vivo recordings due to unknown input currents.
Characterizing intracellular in vivo data requires models that do not rely on controlled input currents.

Purpose of the Study:

To propose a novel single neuron model tailored for characterizing the statistical properties of in vivo recordings.
To develop a model capable of analyzing neuronal activity without precise knowledge of input currents.

Main Methods:

A stochastic process model where subthreshold membrane potential follows a Gaussian process.
Spike emission intensity is a nonlinear function of membrane potential and spiking history.
Model fitting to in vivo data without overfitting.

Main Results:

The model demonstrates a rich dynamical repertoire, capturing arbitrary subthreshold autocovariance functions and firing-rate adaptations.
The model can represent diverse action potential shapes.
Efficient data fitting without overfitting was achieved.

Conclusions:

The proposed model effectively characterizes statistical properties of in vivo neuronal recordings.
This novel model allows for precise comparison of intracellular recordings across different animals and experimental conditions.
It offers a valuable tool for analyzing complex neural data in vivo.