CAREER: Statistical Analysis of Neural Spiking Data

Trustees Of Boston University, Boston

Investigators

Abstract

Neurons represent information about relevant biological signals in patterns of electrical spiking activity. Statistical modeling and dynamical systems theory represent two distinct approaches to modeling the activity of neurons, which have largely been developed separately. However, the neural code is both stochastic and dynamic. This research and education program provides a link between modeling and data analysis by developing a robust framework for the statistical analysis of neural spiking data. This research is partially motivated by the increasing recognition that dynamics related to rhythmic and oscillatory brain signals play an important role in many cognitive states such as attention, memory, perception, and language, while abnormalities in these rhythms are associated with neurological diseases such as schizophrenia, Parkinson's, and Alzheimer's disease. The fundamental objective of this research is to develop a framework for the analysis of neural spike train data that incorporates dynamical state models of neural and other biological signals. This objective will be achieved by a combination of theoretical development of dynamical models and mathematical algorithms, and the application of this theory to the analysis of recorded neural data at multiple scales, including individual neurons, small microcircuits, and larger networks across brain regions. The theoretical component of this research project will extend earlier results using point process methods to more accurately describe and more efficiently extract information from spike trains. New stochastic neural models that include dynamic state variables will be explored, and estimation algorithms based on point process likelihoods and posterior distributions will be derived. This framework will then be applied to problems of relating spiking activity to dynamic oscillatory signals in spiny stellate cells from the medial entorhinal cortex of the rat and in subthalamic nucleus in patients with Parkinson's disease. The theory will inform the applications by providing methods for neural estimation and model evaluation, while these applications will inform the theory by promoting the development of new physiologically relevant neural models.