Abstract:
In this thesis, we studied the fundamental question in neuroscience: how per ception is built based on the sensory stimuli from the physical world and turned into motor actions in the face of uncertain neural representations. The vast body of lit erature contains models using neural activity to decode stimulus parameters, motor responses, and behavioral patterns. In particular, this line of research became more important as sensorimotor neuroprostheses and brain-computer interfaces (BCI) were made possible by recent technology. The main goal of the thesis is to use Bayesian models to understand sensorimotor processing and develop a novel approach for future BCIs. Speci cally, spike data were collected from behaving rats during a yes/no de tection task. Within a Bayesian framework, priors and posterior beliefs are calculated to match the observed choice of the animal. The random variables for stimulus pre sentation, neural activity, and motor responses were combined in a probabilistic graph network. First, a somatosensory neuroprosthesis application is demonstrated. Next, the Bayesian model was used to predict trial-by-trial responses o ine. It was found that psychophysically low-performing rats could be modelled better with the Bayesian approach. These results were compared to predictions of other supervised learning algorithms. The Bayesian prediction was among the best performing algorithms for low-performing rats. Finally, to reveal the e ect of choice history on the current trial's response prediction, previous responses were also included into various Bayesian models incrementally. The results showed that the mean ring rates in a population of neurons are mostly adequate to predict lever presses with high sensitivity and low bias. This thesis provides new insights into computational modeling to understand sensorimotor processing and development of future BCIs. Bayesian modeling can be particularly useful in rehabilitation and during the training period of neuroprostheses.
