Multivariate Encoding Analysis of Medial Prefrontal Cortex Cortical Activity during Task Learning

Studies have shown that medial prefrontal cortex (mPFC) is responsible for outcome evaluation. Some recent studies also suggest that mPFC may play an important role in goal planning and action execution when performing a task. If the information encoded in mPFC can be accurately extracted and identified, it can improve the design of brain-machine interfaces by better reconstructing subjects’ motion intention guided by reward information. In this paper, we investigate whether mPFC neural signals simultaneously encode information of goal planning, action execution and outcome evaluation. Linear-nonlinear-Poisson (LNP) model is applied for encoding analysis on mPFC neural spike data when a rat is learning a two-lever-press discrimination task. We use the L²-norm of tuning parameter in LNP model to indicate the importance of the encoded information and compare the spike train prediction performance of LNP model using all information, the most significant information and reward information only. The preliminary results indicate that mPFC activity can encode simultaneously the information of goal planning, action execution and outcome evaluation and that all the relevant information could be reconstructed from mPFC spike trains on a single trial basis.