A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

TL;DR

This paper introduces a scalable semiparametric Bayesian model that detects dependencies and synchrony among multiple neurons by modeling their spike train data with Gaussian processes and copulas, capturing both firing rates and temporal relationships.

Contribution

The paper presents a novel combination of Gaussian process and copula models for flexible, scalable inference of neuronal dependencies, including lagged synchrony, in spike train data.

Findings

Successfully captured temporal dependencies in simulated data

Identified synchronous neurons in rat prefrontal cortex data

Demonstrated scalability to high-dimensional neuronal data

Abstract

We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their co-firing (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1's (spike) and 0's (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a Gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows: the nonparametric component (i.e., the Gaussian process model) provides a flexible framework for modeling the underlying firing rates; the parametric component (i.e., the copula model) allows us to make inference regarding both contemporaneous and lagged relationships among neurons; using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of dependence structure among variables; our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas in rat's brain.