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A Tractable Method for Describing Complex Couplings between Neurons and Population Rate.

Christophe Gardella¹, Olivier Marre², Thierry Mora³

¹Laboratoire de Physique Statistique, Centre National de la Recherche Scientifique, École Normale Supérieure, Université Pierre et Marie Curie, 75005 Paris, France; Institut de la Vision, Institut National de la Santé et de la Recherche Médicale, Université Pierre et Marie Curie, 75012 Paris, France.

|August 30, 2016

Summary

This study introduces a simple, computationally tractable probabilistic model to capture complex neuronal population activity and interactions. The model accurately reproduces individual neuron firing rates and their dependence on population activity, even nonlinear relationships.

Keywords:

maximum entropy models population couplings retinal ganglion cells tuning curve

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Neural Coding

Background:

Neuronal populations exhibit strong correlations, but simple models to capture these interactions are lacking.
The population rate (summed activity of all neurons) influences individual cell activity, yet explicit models are needed.

Purpose of the Study:

To develop a tractable probabilistic model for population activity that captures firing rates and their dependencies.
To model the linear and nonlinear coupling between individual neuron firing rates and population activity.

Main Methods:

Developed a probabilistic model of population activity.
Inferred model parameters for a 160-neuron population in the salamander retina.
Designed a generalized model to account for nonlinear dependencies.

Main Results:

The model successfully reproduced individual cell firing rates and population rate distributions.
Observed unexpected, nonlinear dependencies of single-cell firing rates on population rate.
Identified specific neurons with preferred population rates.

Conclusions:

A simple, computationally tractable model can capture complex neuronal population dynamics.
The developed model accounts for both linear and nonlinear dependencies between neurons and population activity.
Provides a novel method for analyzing neural population coding.