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DTSTART;TZID=America/New_York:20251010T113000
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DTSTAMP:20260621T161859
CREATED:20250923T155316Z
LAST-MODIFIED:20251007T171403Z
UID:1999-1760095800-1760101200@arni-institute.org
SUMMARY:CTN: Maryam Shanechi
DESCRIPTION:Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology \nAbstract: A major challenge in neuroAI is to model\, decode\, and modulate the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. Toward addressing this challenge\, I will present our work on novel dynamical models of neural-behavioral data and applying them to enable a new generation of brain-computer interfaces for disorders such as major depression. First\, I will present a novel dynamical modeling framework that jointly describes neural-behavioral data\, dissociates behaviorally relevant neural dynamics\, and learns them more accurately. Then\, I will show how we can also predict the effect of inputs\, such as sensory stimuli or neurostimulation\, to dissociate intrinsic and input-driven neural dynamics. I further present how these models can incorporate multiple spatiotemporal scales of brain activity simultaneously\, from spikes to LFP to brain-wide neuroimaging. Finally\, I will discuss the challenge of developing AI algorithms for neurotechnology. I will present a framework that combines neural networks with stochastic state-space models to enable accurate yet flexible inference of brain states causally\, non-causally\, and even with missing neural samples. The above dynamical models can enable next-generation AI-driven neurotechnologies that restore lost motor and emotional function in diverse brain disorders such as paralysis and major depression.
URL:https://arni-institute.org/event/ctn-maryam-shanechi/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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