ArticleJournal of neuroscience methods2017
Tracking recurrence of correlation structure in neuronal recordings.
Article in Journal of neuroscience methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Remapping revisited: how the hippocampus represents different spaces.Nature reviews. Neuroscience · 2024Review
- A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields.Cell reports · 2023Article
- Hyper-Rigid Phasic Organization of Hippocampal Activity But Normal Spatial Properties of CA1 Place Cells in the Ts65Dn Mouse Model of Down Syndrome.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Is hippocampal remapping the physiological basis for context?Hippocampus · 2020Review
- Hippocampal state transitions at the boundaries between trial epochs.Hippocampus · 2020Article
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- Normal CA1 Place Fields but Discoordinated Network Discharge in a Fmr1-Null Mouse Model of Fragile X Syndrome.Neuron · 2018Article
- Phencyclidine Discoordinates Hippocampal Network Activity But Not Place Fields.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCorrelated neuronal activity in the brain is hypothesized to contribute to information representation, and is important for gauging brain dynamics in health and disease. Due to high dimensional neural datasets, it is difficult to study temporal variations in correlation structure. NEW
methodWe developed a multiscale method, Population Coordination (PCo), to assess neural population structure in multiunit single neuron ensemble and multi-site local field potential (LFP) recordings. PCo utilizes population correlation (PCorr) vectors, consisting of pair-wise correlations between neural elements. The PCo matrix contains the correlations between all PCorr vectors occurring at different times.
resultsWe used PCo to interpret dynamics of two electrophysiological datasets: multisite LFP and single unit ensemble. In the LFP dataset from an animal model of medial temporal lobe epilepsy, PCo isolated anomalous brain states, where particular brain regions broke off from the rest of the brain's activity. In a dataset of rat hippocampal single-unit recordings, PCo enabled visualizing neuronal ensemble correlation structure changes associated with changes of animal environment (place-cell remapping). COMPARISON WITH EXISTING METHOD(S): PCo allows directly visualizing high dimensional data. Dimensional reduction techniques could also be used to produce dynamical snippets that could be examined for recurrence. PCo allows intuitive, visual assessment of temporal recurrence in correlation structure directly in the high dimensionality dataset, allowing for immediate assessment of relevant dynamics at a single site.
conclusionsPCo can be used to investigate how neural correlation structure occurring at multiple temporal and spatial scales reflect underlying dynamical recurrence without intermediate reduction of dimensionality.
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