Mixed observation filtering for neural data

Electrophysiological recordings of brain activity include point process spike trains as well as continuous valued signals such as electroencephalograms (EEG), electrocorticograms (ECoG), and local field potentials (LFP). The brain represents information about the outside world in neural spiking activity, which is reflected in each of these signal modalities. An important problem in neuroscience data analysis involves estimating dynamic biological and behavioral signals from neural recordings. Here, we develop an adaptive filtering paradigm for estimating dynamic state processes from mixed observation processes that contain both point process and continuous valued observations. In our analysis of these filtering algorithms, we draw analogies to well-studied linear estimation algorithms such as the Kalman and Extended Kalman filters. We demonstrate the application of this mixed filtering paradigm to the problem of estimating a reaching movement trajectory from simulated simultaneously recorded motor cortical spiking and LFP activity. We demonstrate that the mixed filter is better able to capture information about the movement trajectory than are filters based on the spiking activity or LFPs alone.