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ARNI Distinguished Seminar Series
We are excited to announce the inaugural seminar for the ARNI Distinguished Seminar Series. This will be the first in a series of seminars focused on cutting-edge topics in AI research, innovation, and applications.
Peter de Florez Professor, Brain and Cognitive Sciences; Director, MIT Quest for Intelligence
Location: Zuckerman Institute – Kavli Auditorium (9th Floor)- 3227 Broadway
Date and Time: November 1st at 3:00pm
Zoom: Link
Title:
Do contemporary, machine-executable models (aka digital twins) of the primate ventral visual system unlock the ability to non-invasively, beneficially modulate high level brain states?
Abstract:
In this talk, I will first briefly review the story of how neuroscience, cognitive science and computer science (“AI”) converged to create specific, image-computable, deep neural network models intended to appropriately abstract, emulate and explain the mechanisms of primate core visual object identification and categorization behaviors. Based on a large body of primate neurophysiological and behavioral data, some of these network models are now the most accurate emulators of the primate ventral visual stream — they well-approximate both its internal neural mechanisms and how those mechanisms support the ability of humans and other primates to rapidly and accurately infer object identity, position, pose, etc. from the set of pixels (image) received during typical natural viewing.
Because these leading neuroscientific emulator models — aka “digital twins” — are fully observable and machine-executable, they offer predictive and potential application power that our field’s prior conceptual models did not. I will describe two recent examples from our team. First, the current leading digital twins predict that the brain’s high level visual neurons (inferior temporal cortex, IT) should be highly susceptible to “adversarial attacks” in which an agent (the adversary) aims to strongly disrupt the normal neural response (here, neural firing rate) to any given natural image via small magnitude, targeted changes to that image. We verified this surprising prediction in monkey IT neurons. Second, we show how we can turn this result around and extend it: instead of making adversarial “attacks”, we propose using digital twin models to support non-invasive, beneficial brain modulation. Specifically, we show that we can use a digital twin to design spatial patterns of light energy that, when applied to the organism’s retina in the context of ongoing natural visual processing, results in precise modulation (i.e. rate bias) of the pattern of a population of IT neurons (where any intended modulation pattern is chosen ahead of time by the scientist). Because the IT visual neural populations are known to directly modulate downstream neural circuits involved in mood and anxiety, we speculate that this could provide a new, non-invasive application avenue of potential future human clinical benefit.