Coordinating structure and function for neuronal computations mediating context-dependent behavior

This cooperative agreement from the National Institute of Neurological Disorders and Stroke will fund $8,473,173 for research exploring the neural population codes that support context-dependent behavior. The funding will support a collaborative project between New York University, Riken, and Ecole Normale Superieure from April 15, 2021 through March 31, 2024 under the National Institutes of Health's Extramural Research Programs in the Neurosciences and Neurological Disorders program (CFDA 93.853).

The awardee and sub-awardees will develop a computational framework to understand how biologically-defined neuronal populations shape computations through low-dimensional dynamics to mediate flexible behavior. Riken will perform calcium imaging of rodent neurons and supply data to Ecole Normale Superieure for computational modeling. Ecole Normale Superieure will develop the computational theory and analyze experimental data. New York University will conduct experiments in rodents and non-human primates to validate model predictions and analyze how genetically-identified prefrontal cortex cell types correspond to computations and control context-dependent behavior. The project aims to link neural computation to biology across species to explain how circuit mechanisms give rise to neuronal computations mediating context-dependent behavior.

Abstract

This proposal explores an emergent computational framework for understanding the neural population codes that support flexible, context-dependent behavior. The current state of the field is based on two competing views. According to the circuits view, fixed behaviors arise from specific anatomically or genetically defined cell populations that serve specific functions. Alternatively, the network computation view instead holds that neural activity provides mixed representations of task variables and can be understood only based on the joint activation of many neurons. Currently, these competing views are pursued by different communities with different tools, different behavioral paradigms and different model organisms. This has led to a disconnect between neural computation and the underlying biological circuit mechanisms. Here we propose a unified framework, in which the combinatorial activity of biologically-identified populations of neurons shapes the computations through low dimensional dynamics. A new interdisciplinary team of investigators - pesaran (NYU - primate experimentalist), johansen (riken - rodent experimentalist) and ostojic (ecole normale superieure - computational theory) will develop the computational theory and apply IT to flexible input-output tasks in multiple species - rats and non-human primates. To achieve these goals, we will analyze recurrent neural network models trained to perform a sensory-motor context-dependent decision-making task and fit low-dimensional models to experimental data. We will perform experiments in rodents and non-human primates to validate model predictions that combinatorial coding can support context-dependent behavior and is behaviorally-significant (aim 1). We will analyze whether biologically-defined cell types map onto computationally-defined cell classes during context- dependent behavior by determining how activity in combinations of genetically and anatomically identified PFC cell types corresponds to low-dimensional dynamics (aim 2). In parallel, we will determine how biologically- defined cell types control context-dependent behavior during explicitly-cued and implicitly-signaled contexts by performing optogenetic perturbations of anatomically-defined PFC cell types and testing behavioral performance (aim 3). Successful completion of these aims will link neural computation to biology across multiple species to deliver a computational framework explaining how biological circuit mechanisms give rise to neuronal computations that mediate context-dependent behavior.