Elizabeth Jun (Nuyujukian Lab) & Lavonna Mark (Giocomo Lab)
Elizabeth Jun
PhD Student, Nuyujukian Lab, Stanford University (profile, lab website)
Elizabeth Jun is a neuroscience PhD Candidate at Stanford University and NIH F31 Predoctoral Fellow funded through National Institute of Neurological Disorders and Stroke (NINDS). Her research interests lie in understanding how large networks of neurons coordinate together to perform computations and support a wide range of behaviors in different contexts. Specifically in her thesis work, advised by Dr. Paul Nuyujukian in the Brain Interfacing Laboratory, she aims to understand how the brain can flexibly support a wide range of complex movements that are supported by a fixed set of fundamental motor computations. She is interested in connecting features of high-dimensional population data to similar behaviors in different contexts, and exploring in general how high-dimensional population features relate back to properties of the neural circuit.
Investigating preserved neural motifs across and within tasks in premotor cortex
Abstract
Motor systems neuroscience studies how populations of motor cortical neurons coordinate to perform motor computations, but less is known about how large repertoires of movements are flexibly generated from a fixed set of fundamental motor computations. There is an emerging idea of conserved neural “modules”, or recurring neural activity patterns deploying computations through dynamics, that may serve as elementary building blocks for performing the same fundamental computation across a wide array of contexts. However, exactly what feature of neural dynamics is “recurring” and fundamentally conserved, if any, when performing the same motor computation within a wide range of reach sequence and kinematic contexts has yet to be shown and characterized empirically.
To investigate this, we trained an animal implanted with a 96-channel Utah array in premotor cortex to perform 4 visually guided arm reaching motor tasks that are composed of unique combinations of 5 distinct motor processes (preparation, initiation, execution, hold, error correction) implemented in various reach conditions, where the same process can reappear across tasks and reappear again within tasks. This design allows us to test the hypothesis that premotor dynamics reuse conserved neural dynamics for performing the same motor computational process, but potentially implemented in different locations and orientations in population state space when reperformed across tasks, repeated in a sequence within tasks, and in different kinematic conditions. In this talk, I will explore systematically testing this hypothesis for all 5 motor processes to understand whether there are general organizing principles for premotor dynamics associated with deploying the same fundamental motor computation in a range of tasks, sequences, and kinematic contexts.
Speaker: Lavonna Mark -Talk details coming soon
PhD Student, Giocomo Lab, Stanford University (profile, lab website)
Continue the conversation: Join the speaker for a complimentary dinner in the Theory Center after the seminar