CRCNS Research Proposal: Network models of cortical and subcortical interactions for dynamical control of decision making

University Of California-Davis, Davis CA

Investigators

Abstract

Decisions in the brain are collectively made by a network of interacting brain regions that each play different roles in the decision-making process. These distributed networks are flexible, able to respond effectively to changing circumstances, while also highly robust, and able to preserve functionality even when partially disrupted or damaged. This project seeks to understand how these brain regions interact with each other during decision making and how these interactions confer their remarkable flexibility and robustness. To do this, the research team will combine cutting edge technologies for recording and modifying the activity of neurons while rats make decisions, with machine learning techniques for modeling the data generated. The project will improve understanding of brain systems that support decision making and cognition more generally, while also providing critical insight for the next generation of brain-inspired artificial intelligence systems. The proposal combines large-scale multi-region recordings in awake, behaving rats and data-driven recurrent neural network modeling to investigate the role of association cortex during decision-making through its impact on interacting subcortical areas. The first part of the project will use Neuropixels recordings along with multi-area recurrent neural network modeling to identify whether and how association cortex plays a role in controlling interconnected subcortical dynamics during decision making. The recordings will be targeted to two areas of association cortex and two subcortical regions while rats perform decision tasks that require flexible integration of noisy sensory information over time. The network modeling will be used to disambiguate competing hypotheses for the specific roles of each brain region in shaping neural dynamics during decision formation. The second part of the project will combine similar network modeling with experimental perturbation of brain activity through optogenetics to identify mechanisms that underlie the robustness of neural decision making. The investigators will identify mechanisms of structural robustness, which arise from the architecture of the distributed network itself, and mechanisms of dynamic robustness, which involve active compensation for perturbation. In this manner, the project will synergize recent exciting advances in both machine learning and tools for systems neuroscience to create a tight experiment-theory loop to address questions with broad interdisciplinary importance, with implications at the core of developing principled treatments for cognitive disorders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.