ArticleJournal of neuroscience methods2018
Functional network stability and average minimal distance - A framework to rapidly assess dynamics of functional network representations.
Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- Cholinergic modulation of neural networks supports sequential and complementary roles for NREM and REM states in memory consolidation.PLoS computational biology · 2025Article
- Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation.PLoS computational biology · 2021Article
- The Engram's Dark Horse: How Interneurons Regulate State-Dependent Memory Processing and Plasticity.Frontiers in neural circuits · 2021Review
- Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep.Frontiers in network physiology · 2021Article
- Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss.Cerebral cortex (New York, N.Y. : 1991) · 2018Article
- Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundRecent advances in neurophysiological recording techniques have increased both the spatial and temporal resolution of data. New methodologies are required that can handle large data sets in an efficient manner as well as to make quantifiable, and realistic, predictions about the global modality of the brain from under-sampled recordings. NEW
methodTo rectify both problems, we first propose an analytical modification to an existing functional connectivity algorithm, Average Minimal Distance (AMD), to rapidly capture functional network connectivity. We then complement this algorithm by introducing Functional Network Stability (FuNS), a metric that can be used to quickly assess the global network dynamic changes over time, without being constrained by the activities of a specific set of neurons.
resultsWe systematically test the performance of AMD and FuNS (1) on artificial spiking data with different statistical characteristics, (2) from spiking data generated using a neural network model, and (3) using in vivo data recorded from mouse hippocampus during fear learning. Our results show that AMD and FuNS are able to monitor the change in network dynamics during memory consolidation. COMPARISON WITH OTHER
methodsAMD outperforms traditional bootstrapping and cross-correlation (CC) methods in both significance and computation time. Simultaneously, FuNS provides a reliable way to establish a link between local structural network changes, global dynamics of network-wide representations activity, and behavior.
conclusionsThe AMD-FuNS framework should be universally useful in linking long time-scale, global network dynamics and cognitive behavior.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.