Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping. (arXiv:2210.11478v1 [q-bio.NC])

Objective: A major challenge in closed-loop brain-computer interfaces (BCIs)
is finding optimal stimulation patterns as a function of ongoing neural
activity for different subjects and objectives. Traditional approaches, such as
those currently used for deep brain stimulation, have largely followed a trial-
and-error strategy to search for effective open-loop stimulation parameters, a
strategy that is inefficient and does not generalize to closed-loop
activity-dependent stimulation. Approach: To achieve goal-directed closed-loop
neurostimulation, we propose the use of brain co-processors, devices which
exploit artificial intelligence (AI) to shape neural activity and bridge
injured neural circuits for targeted repair and rehabilitation. Here we
investigate a specific type of co-processor called a “neural co-processor”
which uses artificial neural networks (ANNs) to learn optimal closed-loop
stimulation policies. The co-processor adapts the stimulation policy as the
biological circuit itself adapts to the stimulation, achieving a form of
brain-device co-adaptation. We tested the neural co-processor’s ability to
restore function after stroke by simulating a variety of lesions in a
previously published cortical model of grasping. Main results: Our results show
that a neural co-processor can restore reaching and grasping function after a
simulated stroke in a cortical model, achieving recovery towards healthy
function in the range 75-90%. Significance: This is the first proof-of-concept
demonstration, using computer simulations, of a neural co-processor for
activity-dependent closed-loop neurosimulation for optimizing a rehabilitation
goal after injury. Our results provide insights on how such co-processors may
eventually be developed for in vivo use to learn complex adaptive stimulation
policies for a variety of neural rehabilitation and neuroprosthetic



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