Analysis of complex neural circuits with nonlinear multidimensional hidden state models

Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear in...

Statistical analysis of neural data: Hidden Markov models and applications to multi-state neural models

Analysis of Drifting Dynamics with Neural Network Hidden Markov Models

K. Pawelzik MPI f. Stromungsforschung Bunsenstr. 10 37073 Gottingen, Germany We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method p...

State Sequence Analysis in Hidden Markov Models

Given a discrete time finite state hidden Markov model (HMM) and a sequence of observations, there are different ways to estimate the hidden behavior of the system. In this paper, the problem of finding the most probable state sequence is considered. The state sequence, as opposed to the state trajectory, specifies the sequence of states that the HMM visits but does not specify the dwelling tim...

Tracking with Dynamic Hidden-State Shape Models

Hidden State Shape Models (HSSMs) were previously proposed to represent and detect objects in images that exhibit not just deformation of their shape but also variation in their structure. In this paper, we introduce Dynamic Hidden-State Shape Models (DHSSMs) to track and recognize the non-rigid motion of such objects, for example, human hands. Our recursive Bayesian filtering method, called DP...