ArticleNetwork neuroscience (Cambridge, Mass.)2017
Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity.
Article in Network neuroscience (Cambridge, Mass.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Organizational Principles of Biological Systems.Biology · 2026Review
- Connectivity and function are coupled across cognitive domains throughout the brain.Network neuroscience (Cambridge, Mass.) · 2026Article
- Deciphering the Code of Viral-Host Adaptation Through Maximum-Entropy Nucleotide Bias Models.Molecular biology and evolution · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain.Physical review. X · 2024Article
- Quantifying heterogeneity to drug response in cancer-stroma kinetics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model.Scientific reports · 2022Article
- Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Bridging Single Neuron Dynamics to Global Brain States.Frontiers in systems neuroscience · 2019Article
- Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure.PLoS computational biology · 2018Article
- Blindfold learning of an accurate neural metric.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity.Network neuroscience (Cambridge, Mass.) · 2017Article
- Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings.Journal of computational neuroscience · 2016Article
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5 authors.
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Abstract
Functional coupling networks are widely used to characterize collective patterns of activity in neural populations. Here, we ask whether functional couplings reflect the subtle changes, such as in physiological interactions, believed to take place during learning. We infer functional network models reproducing the spiking activity of simultaneously recorded neurons in prefrontal cortex (PFC) of rats, during the performance of a cross-modal rule shift task (task epoch), and during preceding and following sleep epochs. A large-scale study of the 96 recorded sessions allows us to detect, in about 20% of sessions, effective plasticity between the sleep epochs. These coupling modifications are correlated with the coupling values in the task epoch, and are supported by a small subset of the recorded neurons, which we identify by means of an automatized procedure. These potentiated groups increase their coativation frequency in the spiking data between the two sleep epochs, and, hence, participate to putative experience-related cell assemblies. Study of the reactivation dynamics of the potentiated groups suggests a possible connection with behavioral learning. Reactivation is largely driven by hippocampal ripple events when the rule is not yet learned, and may be much more autonomous, and presumably sustained by the potentiated PFC network, when learning is consolidated.
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