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

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 modeled 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.

Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model.

Across a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These "preparatory states" largely determine the subsequent evolution of both neural activity and behavior, and their importance raises questions regarding how they are, or ought to be, set. Here, we formulate motor preparation as optimal anticipatory control of future movements and show that the solution requires a form of internal feedback control of cortical circuit dynamics. In contrast to a simple feedforward strategy, feedback control enables fast movement preparation by selectively controlling the cortical state in the small subspace that matters for the upcoming movement. Feedback but not feedforward control explains the orthogonality between preparatory and movement activity observed in reaching monkeys. We propose a circuit model in which optimal preparatory control is implemented as a thalamo-cortical loop gated by the basal ganglia.

Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics.

A major challenge in systems neuroscience is to understand how the dynamics of neural circuits give rise to behaviour. Analysis of complex dynamical systems is also at the heart of control engineering, where it is central to the design of robust control strategies. Although a rich engineering literature has grown over decades to facilitate the analysis of such systems, little of it has percolated into neuroscience so far. Here, we give a brief introduction to a number of core control-theoretic concepts that provide useful perspectives on neural circuit dynamics. We introduce important mathematical tools related to these concepts, and establish connections to neural circuit analysis, focusing on a number of themes that have arisen from the modern 'state-space' view on neural population dynamics.

Null Ain't Dull: New Perspectives on Motor Cortex.

Classical work has viewed primary motor cortex (M1) as a controller of muscle and body dynamics. A recent brain-computer interface (BCI) experiment suggests a new, complementary perspective: M1 is itself a dynamical system under active control of other circuits.