A generative spike prediction model using behavioral reinforcement for re-establishing neural functional connectivity

Prediction models that generate neuronal spikes from upstream neural activities offer a promising way to re-establish neural functional connectivity. Traditional methods train these models by supervised learning, which requires downstream recordings as ground truth. However, functional downstream activity cannot be recorded when neurological disorders exist. Here we introduce a reinforcement learning (RL)-based point process framework to generate spike trains that directly maximize behavior-level rewards, thus bypassing downstream recordings. This yields a generative spike model that directly transforms upstream activity into spike patterns modulated to desired behavior. We show that these RL-based generative models produce movement-modulated spike patterns akin to downstream recordings from healthy subjects, providing a biomimetic spike encoding framework. This RL framework outperforms existing methods and demonstrates a strong adaptation capability across different decoder settings, highlighting its potential for neural prostheses in restoring transregional communication with biomimetic cortical stimulation.