Temporal Evolution of Both Premotor and Motor Cortical Tuning Properties Reflect Changes in Limb Biomechanics

A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery and therefore its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortices exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought

Neural computation in the context of upstream dynamics in the retina

Thesis (Ph.D.)--University of Washington, 2021To understand neural circuit function, one would like to understand individual neurons' computation in the context of their physiological inputs. But these inputs are themselves subject to complex dynamics, and we rarely have tools to experimentally control synaptic inputs under physiological conditions that preserve their temporal features. Primary sensory structures present an exception because primary receptor neurons can be controlled experimentally under physiological conditions. Here, I present work carried out in the retina, where signaling by photoreceptors (the primary receptor neurons in vision) has been characterized in detail and can be controlled with light, and computation in downstream circuitry can therefore be investigated in the context of physiological input from photoreceptors. I first present a hybrid biophysical-statistical model of retinal output that disentangles the computational contributions of photoreceptors from those of other circuit elements and successfully predicts retinal ganglion cell responses to stimuli with dynamically changing statistics. Second, I present an investigation of synaptic specializations that could mediate parallel processing of input from different photoreceptor types within individual post-synaptic neurons