Decoding Ensemble Spike States from Extracellular Field Potentials

Behaviors are encoded by multi-scale brain signals, from microscopic spike signals to macroscopic extracellular Field Potentials (FPs). Extracting neuronal spike information from FPs is an important, yet challenging problem. Because FPs stem from summed contributions of a large population of neurons. Previous work inferred single-neuron spiking activity from the FPs using a generalized linear model (GLM). However, FPs reflect the states of neural ensembles more than single-neuron spike trains. In this paper, we propose a computational model to decode ensemble spike states from FPs. This framework first extracts transient features in FPs, and then detects typical ensemble spike patterns and assigns state labels accordingly. Finally, we use a neural network to decode the ensemble spike states from the FP neuromodulations. This FP-Spike decoder is tested on the FP and spike data from the M1 area of an SD rat. We show that our model can effectively decode multi-neuron spike states. Compared with the GLM method for single-neuron spike prediction, our model exhibits 37% less ensemble spike pattern decoding error. These preliminary results show that we can decode informative spike states from FPs, indicating that the decode results can further benefit long-term stable brain-machine interfaces.