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.

No abstract

One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain–machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.

One of the extraordinary characteristics of the biological brain is its low energy expense to implement a variety of biological functions and intelligence compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as the contributor for the brain to run with a low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how the brain learns to generate it have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm, by learning which two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1). Moreover, most neurons that were not directly assigned for controlling the BMI did not boost their excitability, demonstrating an overall energy-efficiency manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting a cohesive rather than dissociable processing underlie the refinement of energy-efficient temporal patterns.