Topological Identification and Analysis of Cyclic Features in Neural Population Coding
Abstract
A fundamental goal in theoretical systems neuroscience is the creation of a coherent quantitative framework for describing how neural systems encode and process information. This proposal aims contribute to the development of such a framework by leveraging the common stimulus space model for neural population coding to study how a ubiquitous population activity profile called cyclicity is represented within and across neural populations. Building on previous work by the PI using techniques from the emerging field of applied algebraic topology to characterize population dynamics, we will develop a general tool kit for the rigorous and systematic study of neural coding of cyclic features. Specifically, we will (1) develop theoretical and software tools to rigorously identify and verify correspondences between cyclic features coded by related neural populations or expressed in response to cyclic features of external stimuli; (2) investigate how the structure of feed-forward networks permits and inhibits the propagation of coded cyclic features between connected neural systems, via both theoretical analysis and computational experiments; and (3) develop a methodology for designing closed-loop activity regulation control systems inside recurrent neural networks. This research will provide a novel theoretical framework for the study of coding in in vivo and artificial neural systems, address open questions about the structure-function relationship in neural populations and about how computation occurs among distinct neural systems, and yield freely available software that will allow researchers to perform these analyses independently It will provide experimentalists with novel techniques for studying how cyclicity in neural activity influences behavior, understanding coding properties of deep brain networks, and developing closed-loop control methods for interrogation of system function. Finally, the underlying mathematical methods being developed are not specific to populations of neurons and will be applicable to population activity and control system problems in many domains.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110266XX0
Entities
People
- Chad Giusti
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Delaware