In search for the invisible: motor inhibition in monkey premotor cortex and its RNN replicas

Abstract

Controlling actions in dynamically changing environments requires flexible and efficient motor control. A fundamental challenge in neuroscience is to uncover how cortical circuits generate, adjust, and sometimes suppress planned movements. To address this, we combined recordings from dorsal premotor cortex (PMd) of macaque monkeys performing a stop-signal task, with a recurrent neural network (RNN) model inferred directly from multi-unit activity. This data-driven "digital twin" reproduced the cortical population dynamics underlying motor planning and inhibition, revealing how internal network states shape behavior, and generating synthetic neural trajectories for unseen conditions. RNN internal state explained reaction time fluctuations across trials, reflecting stochastic components of motor readiness and endogenous variability of PMd activity. The same pre-Go latent state also constrained movement inhibition modulating the networks response to Stop signals by reshaping the attractor dynamics of a null-potent subspace. These results establish a mechanistic link between latent cortical dynamics and flexible behavioral control, demonstrating how autonomous RNN inference can uncover circuit-level computations.