6 research outputs found
Measuring Shared Information and Coordinated Activity in Neuronal Networks
Most nervous systems encode information about stimuli in the responding
activity of large neuronal networks. This activity often manifests itself as
dynamically coordinated sequences of action potentials. Since multiple
electrode recordings are now a standard tool in neuroscience research, it is
important to have a measure of such network-wide behavioral coordination and
information sharing, applicable to multiple neural spike train data. We propose
a new statistic, informational coherence, which measures how much better one
unit can be predicted by knowing the dynamical state of another. We argue
informational coherence is a measure of association and shared information
which is superior to traditional pairwise measures of synchronization and
correlation. To find the dynamical states, we use a recently-introduced
algorithm which reconstructs effective state spaces from stochastic time
series. We then extend the pairwise measure to a multivariate analysis of the
network by estimating the network multi-information. We illustrate our method
by testing it on a detailed model of the transition from gamma to beta rhythms.Comment: 8 pages, 6 figure
Discovering Functional Communities in Dynamical Networks
Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science"
style. Forthcoming in the proceedings of the workshop "Statistical Network
Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small
clarifications, typo corrections, added referenc
Abstract
Most nervous systems encode information about stimuli in the responding activity of large neuronal networks. This activity often manifests itself as dynamically coordinated sequences of action potentials. Since multiple electrode recordings are now a standard tool in neuroscience research, it is important to have a measure of such network-wide behavioral coordination and information sharing, applicable to multiple neural spike train data. We propose a new statistic, informational coherence, which measures how much better one unit can be predicted by knowing the dynamical state of another. We argue informational coherence is a measure of association and shared information which is superior to traditional pairwise measures of synchronization and correlation. To find the dynamical states, we use a recently-introduced algorithm which reconstructs effective state spaces from stochastic time series. We then extend the pairwise measure to a multivariate analysis of the network by estimating the network multi-information. We illustrate our method by testing i
Discovering Functional Communities in Dynamical Networks
<p>Many networks are important because they are substrates for dynamical systems, and their pattern of functional connectivity can itself be dynamic -- they can functionally reorganize, even if their underlying anatomical structure remains fixed. However, the recent rapid progress in discovering the community structure of networks has overwhelmingly focused on that constant anatomical connectivity. In this paper, we lay out the problem of discovering <em> functional communities</em>, and describe an approach to doing so. This method combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks. We illustrate it with an application to a large biophysical model of the transition from beta to gamma rhythms in the hippocampus.</p