Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Asynchronous, adaptive BCI using movement imagination training and rest-state inference

The current study introduces an adaptive Bayesian learning scheme which discriminates between left hand movement imagination, right hand movement imagination and idle (i.e. "no-command") state in an EEG Brain Computer Interface. Unlike previous BCI designs using minimal training, the user does not have to continuously imagine a movement in order to control a cursor. Rather, the cursor reacts meaningfully only when a trained movement imagination is produced. The algorithmic approach was to compute Gaussian probability distributions in log-variance of main Common Spatial Patterns for each movement class, infer from these a prior distribution of idle-class, and allow each distribution to adapt during feedback BCI performance. By producing a markedly different but complexity constrained partition of feature space than with LDA classifiers, allowing the classifier to adapt and introducing an intermediary state driven by the classifier output through a dynamic control law, 90 % level classification accuracy was achieved with less than 5 seconds activation time from cued onset