Computationally efficient model selection for joint spikes and waveforms decoding

Abstract

A recent paradigm for decoding behavioral variables or stimuli from neuron ensembles relies on joint models for electrode spike trains and their waveforms, which, in principle, is more efficient than decoding from electrode spike trains alone or from sorted neuron spike trains. In this paper, we decode the velocity of arm reaches of a rhesus macaque monkey to show that including waveform features indiscriminately in a joint decoding model can contribute more noise and bias than useful information about the kinematics, and thus degrade decoding performance. We also show that selecting which waveform features should enter the model to lower the prediction risk can boost decoding performance substantially. For the data analyzed here, a stepwise search for a low risk electrode spikes and waveforms joint model yielded a low risk Bayesian model that is 30% more efficient than the corresponding risk minimized Bayesian model based on electrode spike trains alone. The joint model was also comparably efficient to decoding from a risk minimized model based only on sorted neuron spike trains and hash, confirming previous results that one can do away with the problematic spike sorting step in decoding applications. We were able to search for low risk joint models through a large model space thanks to a short cut formula, which accelerates large matrix inversions in stepwise searches for models based on Gaussian linear observation equations.