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UID:20251002T2028Z-1759436938.2252-EO-25871-1@10.73.9.33
STATUS:CONFIRMED
DTSTAMP:20260527T170926Z
CREATED:20251002T202656Z
LAST-MODIFIED:20251002T212229Z
DTSTART;TZID=America/Chicago:20251013T100000
DTEND;TZID=America/Chicago:20251013T110000
SUMMARY: CTCN Seminar Series: Ram Dyuthi Sristi (UCSD)
DESCRIPTION: Ram Dyuthi Sristi from UCSD is presenting a Center for Theoret
 ical & Computational Neuroscience Seminar Series talk.
X-ALT-DESC;FMTTYPE=text/html: <h3>"Uncovering Neural Circuit Dynamics Acros
 s Regions and Contexts"</h3><p><a href="https://scholar.google.com/citation
 s?user=yKZAiCYAAAAJ&hl=en" data-type="link" data-id="https://scholar.google
 .com/citations?user=yKZAiCYAAAAJ&hl=en">Ram Dyuthi Sristi <img class="size-
 full wp-image-25873 alignright" src="https://neuroscience.wustl.edu/app/upl
 oads/2025/10/Ram-Dyuthi-Sristi.jpeg" alt="Ram Dyuthi Sristi has short dark 
 hair and is wearing glasses." width="225" height="275" /><br /></a>PhD Stud
 ent<br />University of California\, San Diego</p><p>Understanding how neura
 l circuits interact across brain regions to encode behavior and adapt under
  changing conditions is a central challenge in neuroscience. Large-scale re
 cordings capture activity across multiple areas\, but extracting interpreta
 ble signals from such data requires new computational tools. I will first p
 resent Coupled Transformer Autoencoder (CTAE)\, a sequence model that disen
 tangles shared versus region-specific dynamics while modeling long-range te
 mporal dependencies. By partitioning latent space into orthogonal shared an
 d private subspaces\, CTAE uncovers inter-regional communication signals an
 d local computations. Applied to motor cortical recordings during a reach t
 ask and multisensory recordings during decision-making\, CTAE improves beha
 vioral decoding over existing methods for multi-region analysis. Next\, I w
 ill introduce Conditional Stochastic Gates (c-STG)\, a contextual feature s
 election method that identifies neuronal subpopulations encoding behavior a
 cross contexts such as task epochs or stimulus conditions. Using a hypernet
 work to map context variables to probabilistic feature-selection gates\, c-
 STG improves both accuracy and interpretability. Applied to motor cortical 
 recordings\, a single c-STG model revealed compact subpopulations that pred
 icted trial outcomes from trial timing or reward type. Together\, these app
 roaches parse shared and context-dependent neural structure\, enabling test
 able hypotheses on circuit reorganization and inter-areal communication.</p
 ><p><a href="https://ctcn.wustl.edu/events/">View all events from the Cente
 r for Theoretical & Computational Neuroscience at WashU »</a></p>
CATEGORIES:CTCN Events
LOCATION:Neuroscience Research Building Auditorium
GEO:38.635602;-90.254892
ORGANIZER;CN="Shea":MAILTO:shea.stewart@wustl.edu
URL;VALUE=URI:https://neuroscience.wustl.edu/events/event/ctcn-seminar-seri
 es-ram-dyuthi-sristi-ucsd/
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DTSTART:20250309T080000
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