Decoding Reward Information from Local Field Potential and Spikes in Medial Prefrontal Cortex of Rats

摘要

Reinforcement learning (RL)-based brain-machine interfaces (BMIs) obtain the mapping between neural activities and the subject’s intention using reward. The advantage is to allow subjects to learn to control the external device without real limb movements. Internal-reward-based RL-BMIs train the decoder based on the reward information extracted from neural activities, which is a step towards autonomous BMI design. Studies have used medial prefrontal cortex (mPFC) activity to extract the internal reward when rodents are in the learning process. However, the reward and non-reward classification using single neuron spikes is noisy. In this paper, we explore the reward interpretation ability of local field potentials (LFPs) in the mPFC area of SD rats, especially on the high-frequency bands. We also investigate whether LFPs contain extra information over spikes by using support vector machine (SVM) as the classifier to distinguish the rewarding and non-rewarding trials. We find that among the three bands, namely, gamma (30–80Hz), high-gamma (80–200Hz) and bhfLFP (200–400Hz), the bhfLFP band has the highest decoding accuracy (86.97% for a high lever task and 79% for a low lever task). Compared with the spike only, the integrated LFP-spike feature has comparable or better classification performance. It potentially provides more stable internal reward for RL-based BMIs.