dorsal/arxiv
View SchemaMeasuring Shared Information and Coordinated Activity in Neuronal Networks
| Authors | Kristina Lisa Klinkner, Cosma Rohilla Shalizi, Marcelo F. Camperi |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0506009 |
| URL | https://arxiv.org/abs/q-bio/0506009 |
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 it on a detailed model of the transition from gamma to beta rhythms.
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"abstract": "Most nervous systems encode information about stimuli in the responding\nactivity of large neuronal networks. This activity often manifests itself as\ndynamically coordinated sequences of action potentials. Since multiple\nelectrode recordings are now a standard tool in neuroscience research, it is\nimportant to have a measure of such network-wide behavioral coordination and\ninformation sharing, applicable to multiple neural spike train data. We propose\na new statistic, informational coherence, which measures how much better one\nunit can be predicted by knowing the dynamical state of another. We argue\ninformational coherence is a measure of association and shared information\nwhich is superior to traditional pairwise measures of synchronization and\ncorrelation. To find the dynamical states, we use a recently-introduced\nalgorithm which reconstructs effective state spaces from stochastic time\nseries. We then extend the pairwise measure to a multivariate analysis of the\nnetwork by estimating the network multi-information. We illustrate our method\nby testing it on a detailed model of the transition from gamma to beta rhythms.",
"arxiv_id": "q-bio/0506009",
"authors": [
"Kristina Lisa Klinkner",
"Cosma Rohilla Shalizi",
"Marcelo F. Camperi"
],
"categories": [
"q-bio.NC",
"math.ST",
"nlin.CD",
"q-bio.QM",
"stat.TH"
],
"title": "Measuring Shared Information and Coordinated Activity in Neuronal Networks",
"url": "https://arxiv.org/abs/q-bio/0506009"
},
"schema_id": "dorsal/arxiv",
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"variant": "snapshot-2026-03-01",
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