Do contemporary, machine-executable models of the primate ventral visual system unlock the ability to non-invasively, beneficially modulate high level brain states?

Over the past decade, 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 ventral visual processing, up to its deepest neural level, the inferior temporal cortex (IT). Because these leading neuroscientific emulation 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.  Our team’s ongoing work is aimed at asking if current digital twin models might support non-invasive, beneficial brain modulation.  In this talk, I will describe a key result:  we demonstrate that we can use a digital twin to design spatial patterns of light energy that, when “added” to the organism’s retinal input 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 connect to and modulate downstream neural circuits (e.g. amygdala) that may underlie psychological affective states (e.g. mood and anxiety), this novel basic science may unlock a new, non-invasive application avenue of potential future human clinical benefit.

This progress and new impact possibilities resulted from convergent brain science and AI engineering efforts in the domain of visual object intelligence.   I will motivate this as just one example of what I believe will unlock in other domains of human intelligence as brain scientists and AI engineers collaborate to develop machine-executable models of the underlying mechanisms of those still-mysterious domains.

Alternate locations:

Online: https://utoronto.zoom.us/j/88523051719