CTN: Maryam Shanechi

Title: Dynamical models of neural-behavioral data with application to AI-driven neurotechnology

Abstract: 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.