Neuromimetic control - a linear model paradigm
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First author draft
Date
2021
DOI
Authors
Baillieul, John
Sun, Zexin
Version
First author draft
OA Version
Citation
J. Baillieul, Z. Sun. 2021. "Neuromimetic Control - A Linear Model Paradigm.." CoRR, Volume abs/2104.12926,
Abstract
Stylized models of the neurodynamics that underpin sensory motor control in animals are proposed and studied.
The voluntary motions of animals are typically initiated by high level intentions created in the primary cortex
through a combination of perceptions of the current state of the environment along with memories of past reactions
to similar states. Muscle movements are produced as a result of neural processes in which the parallel
activity of large multiplicities of neurons generate signals that collectively lead to desired actions. Essential to
coordinated muscle movement are intentionality, prediction, regions of the cortex dealing with misperceptions
of sensory cues, and a significant level of resilience with respect to disruptions in the neural pathways through
which signals must propagate. While linear models of feedback control systems have been well studied over
many decades, this paper proposes and analyzes a class of models whose aims are to capture some of the essential
features of neural control of movement. Whereas most linear models of feedback systems entail a state
component whose dimension is higher than the number of inputs or outputs, the work that follows will treat models
in which the numbers of input and output channels greatly exceed the state dimension. While we begin by
considering continuous-time systems governed by differential equations, the aim will be to treat systems whose
evolution involves classes of inputs that emulate neural spike trains. Within the proposed class of models, the
paper will study resilience to channel dropouts, the ways in which noise and uncertainty can be mitigated by an
appropriate notion of consensus among noisy inputs, and finally, by a simple model in which binary activations
of a multiplicity of input channels produce a dynamic response that closely approximates the dynamics of a
prescribed linear system whose inputs are continuous functions of time.
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