Abstract
Humans and animals can effortlessly coordinate their movements with external stimuli. This capacity indicates that sensory inputs can rapidly and flexibly reconfigure the ongoing dynamics in the neural circuits that control movements. Here, we develop a circuit-level model that coordinates movement times with expected and unexpected temporal events. The model consists of two interacting modules, a motor planning module that controls movement times and a sensory anticipation module that anticipates external events. Both modules harbor a reservoir of latent dynamics, and their interaction forms a control system whose output is adjusted adaptively to minimize timing errors. We show that the model’s output matches human behavior in a range of tasks including time interval production, periodic production, synchronization/continuation, and Bayesian time interval reproduction. These results demonstrate how recurrent interactions in a simple and modular neural circuit could create the dynamics needed to control timing behavior.
Highlights
Humans and animals can effortlessly coordinate their movements with external stimuli
We start by introducing a basic circuit module (BCM) that acts as a flexible open-loop controller for producing desired time intervals
We extend the BCM to a motor planning module (MPM) capable of producing isochronous rhythms
Summary
Humans and animals can effortlessly coordinate their movements with external stimuli. Consistent with this proposal, experiments in animal models have found that neural activity in anticipation of a delayed response reaches a fixed threshold[12,13,14] at a rate that is inversely proportional to the delay period[7,15,16] These results suggest that the brain supports flexible timing by controlling the speed at which neural activity approaches a movement initiation threshold. It was shown that flexible control of speed can be achieved through nonlinear interactions within a simple model consisting of a pair of units with reciprocal inhibitory connections[7] In this model, the speed at which the output evolves toward a movement initiation threshold can be adjusted flexibly via a shared input (Fig. 1a). From a dynamical systems perspective, this model can be viewed as an open-loop controller that converts an instruction (i.e., shared input) to the desired dynamics (i.e., speed)
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