Neural ensembles underlying natural tracking behavior
University Of Texas At Austin, Austin TX
Investigators
Linked publications & trials
Abstract
? DESCRIPTION (provided by applicant): The system that controls smooth pursuit eye movements is one of the most accessible, promising systems for understanding how neural circuits transform sensory inputs into actions. Pursuit is a natural, ecologically- relevant behavir that allows primates to track moving objects of interest in the visual world. Now-classical analyses relating neural activity to behavior have already provided insights about the systems-level functions and computations of the pursuit circuit that perhaps exceed our understanding of all other voluntary behaviors in mammals. Despite these successes, there are large gaps in our understanding of the pursuit system. We seek a new level in understanding the circuit by measuring and manipulating the activity of large populations of identified neurons in the key sensory and prefrontal cortical areas. We intend to address how different neuronal types function during pursuit, how they implement systems-level computations within micro- and meso-circuits, and how control centers select the appropriate sensory data given cognitive factors. These questions, although stated in the context of the pursuit system, are instances of the more general problem of how population activity in large numbers of sensory neurons is parsed and converted into appropriate behaviors. The time is now ripe to take advantage of several technical and conceptual revolutions: Specifically, we propose to study the neural basis of pursuit eye movements in marmosets, in which cortical areas responsible for motion sensation and target selection are easily accessed and for which genomic toolboxes are being generated. We will investigate the functional circuitry at multiple scales using a combination of 2-photon calcium imaging and large-scale extracellular array electrophysiology, cell-type identification and optogenetic perturbation, and dynamical systems modeling.
View original record on NIH RePORTER →