Low-dimensional neural manifolds for the control of constrained and unconstrained movements

Abstract

Across many brain areas, neural population activity appears to be constrained to a low-dimensional manifold within a neural state space of considerably higher dimension. Recent studies of the primary motor cortex (M1) suggest that the activity within the low-dimensional manifold, rather than the activity of individual neurons, underlies the computations required for planning and executing movements. To date, these studies have been limited to data obtained in constrained laboratory settings where monkeys executed repeated, stereotyped tasks. An open question is whether the observed low dimensionality of the neural manifolds is due to these constraints; the dimensionality of M1 activity during the execution of more natural and unconstrained movements, like walking and picking food, remains unknown. We have now found similarly low-dimensional manifolds associated with various unconstrained natural behaviors, with dimensionality only slightly higher than those associated with constrained laboratory behaviors. To quantify the extent to which these low-dimensional manifolds carry task-relevant information, we built task-specific linear decoders that predicted EMG activity from M1 manifold activity. In both settings, decoding performance based on activity within the estimated low-dimensional manifold was the same as decoding performance based on the activity of all recorded neurons. These results establish functional links between task-specific manifolds and motor behaviors, and highlight that both constrained and unconstrained behaviors are associated with low-dimensional M1 manifolds.