When and why does motor preparation arise in recurrent neural network models of motor control?

Abstract

During delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modelled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1. By studying a variety of network architectures, we could dissect and predict the situations in which it is beneficial for a network to prepare. Finally, we show that optimal input-driven control of neural dynamics gives rise to multiple phases of preparation during reach sequences, providing a novel explanation for experimentally observed features of monkey M1 activity in double reaching.

Effects of noise and metabolic cost on cortical task representations

“…In line with previous studies 3,[18][19][20]24,[31][32][33]35,46,47 , we operationalized metabolic cost in our models through L 2 firing rate regularization. This cost penalizes high overall firing rates.…”

“…4 is a firing rate regularization term. This regularization term can be interpreted as a form of energy or metabolic cost 18,24,31,46,47 measured across the whole trial, T , because it penalizes large overall firing rates. Therefore, optimizing this cost function encourages networks to not only solve the task, but to do so using low overall firing rates.…”