The essence of cognition is choice, and to understand choice we need to understand the brain mechanisms that guide decisions in complex settings. These mechanisms are implemented through interactions of large neural networks across cortical and subcortical areas. Tracking population response dynamics on single trials and relating them to internal cognitive states and overt behavior are critical for incisive tests of current models of decision-making. Here we propose to characterize the activity patterns of large populations of neurons (100+) in the prefrontal cortex of macaque monkeys engaged in decision-making tasks. We will explain the spatiotemporal patterns of activation in the population by developing the most parsimonious probabilistic model that takes into account pairwise and higher-order interactions of neurons. The model will be utilized to characterize response manifolds of the network and quantify its dynamics in different task epochs. Our approach is unique because rather than trying to embed the population dynamics in a low dimensional manifold using Machine Learning tools, we propose to extend the Maximum Entropy framework to directly capture the high-dimensional dynamics. Further, by characterizing functional dependencies among cells we can map the architecture and design of large networks in terms of subnetwork motifs and computations. Finally, we will investigate how noisy fluctuations of responses or artificial manipulation of network activity influences its dynamics and modifies or disturbs its computations. Scrutinizing the results of these experiments within our modeling framework makes headway toward addressing long-standing questions about decision-making, including the neural basis of psychological models and effects of initial state on the behavior. Our work will have broader impacts in two domains. First, the path that we will chart for discovering functional subnetworks and their computations will be useable in various subfields of neuroscience. We will significantly advance data analysis and computational modeling tools available to neuroscientists and, therefore, will facilitate future studies of normal mental functions and mental disorders using high-dimensional neural data. Second, characterizing information encoding and response dynamics in the prefrontal cortex sheds light on mechanisms of decision-making and emergence of cognitive abilities in complex neural networks. Deficits of decision-making are at the heart of a number of neurological and psychiatric disorders including schizophrenia, Alzheimer's, and Parkinson's disease. Several behavioral and pharmacological therapies have been proposed for those deficits, but we lack a clear understanding of how they work at the level of neuronal systems. To develop the next generation of therapies, we need to understand how cognitive processes emerge across multiple functional levels, from individual neurons to networks of brain areas. Our work is a step in that direction. It has the potential to advance our understanding of pathology of mental disorders and help with the discovery of better treatments.
The goal of this proposal is to develop a computational framework that accurately captures response dynamics of a population of prefrontal neurons during decision-making. We will simultaneously measure and manipulate the activity of one hundred or more prefrontal neurons and develop computational models that explain the response dynamics of the prefrontal network. Our study will facilitate a better understanding of the decision-making process, which is impaired in a number of neurological and psychiatric disorders including schizophrenia, Alzheimer's, and Parkinson's disease.
