A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

Abstract

The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.

Reconstructing neuronal circuitry from parallel spike trains

“…There could also be fluctuations on an even longer time scale. There are several methods for processing such nonstationarity, including the state-space models 56,57 or the Gaussian process 58 . Such slow fluctuations may induce variation in the CC amplitude, but they would not appear in the averaged cross-correlogram in an interval of 100 ms in our framework.…”

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States

“…Gaussian process have been widely developed in spatial-temporal modeling (Williams and Rasmussen, 2006;Banerjee et al, 2008Banerjee et al, , 2014Gelfand et al, 2005;Quick et al, 2013;Stein, 2012;Zhou et al, 2015;Vandenberg-Rodes and Shahbaba, 2015;Wang and Gelfand, 2014). It provides a framework that can capture the non-linear and stochastic components of exogenous and endogenous variables based on generalized linear models, which makes it useful for modeling binary time series and classification.…”

A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains

“…Furthermore, the Poisson process has very limited multivariate generalizations in terms of dependence structure [12,9,4,11,6,7]. Some alternative models include Integrate-and-Fire models [3,13], GLMs [8,2,17,18], renewal processes [10], and latent variable models [16,19].…”