20. Characterizing neural dependencies with Poisson copula models

Pietro Berkes berkes@gatsby.ucl.ac.uk Jonathan Pillow pillow@gatsby.ucl.ac.uk Frank Wood fwood@gatsby.ucl.ac.uk

Gatsby Computational Neuroscience Unit, UCL, London, UK

The activities of individual neurons in cortex and many other areas of the brain are often well described by Poisson distributions. Unfortunately, there is no simple joint Poisson distribution that can incorporate statistical dependencies (e.g., correlations) between neurons. For this reason, neural population coding models often either assume that the individual neurons are independent, or transform the joint activity mathematically to continuous quantities and model them using a multivariate distribution that naturally encodes dependency, such as the multivariate Gaussian. However, these solutions are sometimes poorly suited to describing neural population responses, failing to match either the marginal distributions of individual neurons or the detailed form of their dependencies. Here we develop a joint model for neural population responses using copulas, which allow Poisson marginal distributions to be combined into a joint distribution that can exhibit various kinds of dependency.

Copulas are mathematical objects that specify a joint distribution’s dependency structure separately from its marginal structure [1]. Copulas provide a principled way to quantify non-linear dependencies that go beyond correlation coefficients, in a manner that is independent of rescaling of individual variables.

Here we present some results on constructing joint distributions for the activity of pairs of neurons by choosing the marginals to be Poisson distributed, selecting an appropriate parametric family of copulas, and fitting the model parameters (of both the marginals and the copula) using Maximum Likelihood estimation. Different copula families are able to capture dependencies of different kinds (e.g., dependencies limited to the lower or upper tails of the distribution, or negative dependencies). The selection of an appropriate parametric family for the copula distribution can be addressed by cross-validation.

This work was supported by the Gatsby Charitable Foundation and the Royal Society USA/Canada Research Fellowship.

[1] R.B. Nelson (1999) An introduction to copulas. Lecture notes in statistics 139, Springer Verlag, New York.