Transitioning from global to local computational strategies during brain-machine interface learning.

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e.

Neural ensemble dynamics in trunk and hindlimb sensorimotor cortex encode for the control of postural stability.

The cortex has a disputed role in monitoring postural equilibrium and intervening in cases of major postural disturbances. Here, we investigate the patterns of neural activity in the cortex that underlie neural dynamics during unexpected perturbations. In both the primary sensory (S1) and motor (M1) cortices of the rat, unique neuronal classes differentially covary their responses to distinguish different characteristics of applied postural perturbations; however, there is substantial information gain in M1, demonstrating a role for higher-order computations in motor control.

Exercise therapy guides cortical reorganization after midthoracic spinal contusion to enhance control of lower thoracic muscles, supporting functional recovery.

Postural control is critical for locomotion, allowing for gait changes, obstacle avoidance and navigation of rough terrain. A major problem after spinal cord injury (SCI) is regaining the control of balance to prevent falls and further injury. While the circuits for locomotor pattern generation reside in the spinal cord, postural control consists of multiple, complex networks that interact at the spinal, brainstem and cortical levels.