Robust Network-level Inference from Neuronal Data Underlying Behavior

University Of Maryland, College Park, College Park MD

Investigators

Abstract

Individual neurons are highly unreliable computational units in isolation, due to their drastic trial-to-trial response variability. Yet, when they act together as a network, they result in robust brain function and precise behavioral outcomes. The advent of large-scale neural recording technologies, such as two-photon calcium imaging, has created a paradigm shift by enabling scientists and engineers to study the activity of large populations of neurons in order to decipher how they collectively encode information from the external world and distill them to elicit robust behavior. In order to fully utilize these data, computationally efficient and mathematically principled techniques for robust network-level inference are required. The research objective of this proposal is to develop such methodologies to infer network-level characteristics of ensemble neuronal activity from two-photon imaging data, and to apply these methods to large-scale recordings in order to reveal the computational principles that underlie sensory processing and behavior. The research approaches include: developing a robust framework for joint inference of the intrinsic and stimulus-driven correlations of neuronal activity, designing a functional taxonomy to characterize the relevance of neuronal activity to sensory processing and behavioral outcomes, and constructing an estimation framework for capturing the dynamics and functional relevance of higher-order synchronous neuronal activity. This project addresses several outstanding challenges faced by existing methodologies, including biased network characterization incurred by two-stage analysis pipelines, intermixing the contributions of exogenous and endogenous processes to collective neuronal activity, and studying sensory processing and behavioral elicitation as disjoint problems. By employing two-photon calcium imaging data from mice and zebrafish, the proposed modeling and estimation framework will be used to investigate several fundamental problems in systems neuroscience such as tonotopic diversity in the auditory cortex, interaction of sensory processing and decision-making, and visuo-motor coordination. The project is expected to impact technology by providing signal processing solutions to be used in neural control and neuromorphic systems. The research is also integrated with educational and outreach activities including high school level workshops, undergraduate involvement in research, and course development. 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.