A Dynamic Bayesian Model for Detecting Neuronal Communities

• Activity Number: 668
• Type: Invited
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Babak Shahbaba* and Bo Zhou and Hernando Ombao and Sam Behseta and David Moorman
• Companies: UC Irvine and UC Irvine and UC Irvine and California State University at Fullerton and University of Massachusetts Amherst
• Keywords: Gaussian process models ; Product partition model ; Neuronal communities ; Decision making ; Spike trains

Abstract

We propose a novel statistical model for detecting neuronal communities involved in decision making. Our method characterizes the activity of multiple neurons during a basic cognitive task by modeling their joint distribution dynamically. In addition to allowing the neuronal activity to change over time, our proposed model can also capture time-varying (non-stationary) interactions among neurons. This way, the functionally-identified neuronal communities are dynamic and can change over time. This provides a more realistic model of the neural systems underlying decision-making processes. By identifying communities (subsets) of neurons that distinguish one type of decision from another, we expect our method to provide insights into the decision-making process in particular as well as into a broad range of cognitive functions. We evaluate our model using several simulation studies and apply it to real data based on an experiment designed for investigating the role of the prefrontal cortex in basic reward-related decision-making behaviors.