Neural Implementation of Precise Temporal Patterns in Motor Cortex

Abstract

One of the most concerned problems in neuroscience is how neurons communicate and convey information through spikes. There is abundant evidence in sensory systems to support the use of precise timing of spikes to encode information. However, it remains unknown whether precise temporal patterns could be generated to drive output in the primary motor cortex (M1), a brain area containing ample recurrent connections that may destroy temporal fidelity. Here, we used a novel brain-machine interface that mapped the temporal order and precision of motor cortex activity to the auditory cursor and reward to guide the generation of precise temporal patterns in M1. During the course of learning, rats performed the “temporal neuroprosthetics” in a goal-directed manner with increasing proficiency. Precisely timed spiking activity in M1 was volitionally and robustly produced under this “temporal neuroprosthetics”, demonstrating the feasibility of M1 implementing temporal codes. Population analysis showed that the local network was coordinated in a fine time scale as the overall excitation heightened. Furthermore, we found that the directed connection between neurons assigned to directly control the output (“direct neurons”) was strengthened throughout learning, as well as connections in the subnetwork that contains direct neurons. Network models revealed that excitatory gain and strengthening of subnetwork connectivity transitioned neural states to a more synchronous regime, which improved the sensitivity for coincidence detection and, thus, the precision of spike patterns. Therefore, our results suggested the recurrent connections facilitate the implementation of precise temporal patterns instead of impairing them, which provided new perspectives on the fine-timescale activity and dynamics of M1.