Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models

Humphries, Mark D.; Caballero, Javier A.; Evans, M. G.; Maggi, Silvia; Singh, Abhinav

doi:10.1371/journal.pone.0254057

Cited by 2 publications

(11 citation statements)

References 44 publications

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“…To test if neural ensembles also showed different structure at fast and slow timescales, we ran a community detection algorithm from network science on the correlation matrices to detect ensembles (Figure 3a) (Humphries, Caballero, Evans, Maggi, & Singh, 2021). The algorithm splits the neurons into non-overlapping subsets based on their correlations, trying to discover ensembles of neurons with strong positive correlations between the members of each ensemble, but weaker correlations with neurons in other ensembles (Methods).…”

Section: Resultsmentioning

confidence: 99%

“…The mice were awake but head-fixed. We wrote Python code to compute the correlation matrices and implement the community detection algorithm (18). Further details are provided in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…Any structure in the data-derived network beyond its degree sequence and weight sequence will not be captured by the weighted configuration model. In practice we used an extension that also preserves sparsity, by sampling from a probability distribution for the creation or non-creation of each possible connection, then distributing the weight of the data network randomly in this sparse network (Humphries et al, 2021). To detect the structure in the data beyond that seen in the null model we used a spectral rejection procedure, as follows.…”

Section: Ensemble Detection Analysismentioning

confidence: 99%

“…A common choice for P in community detection is the 'configuration model' (Fosdick et al, 2018;Humphries et al, 2021).…”

Section: Ensemble Detection Analysismentioning

confidence: 99%

“…clustering method that uses an explicit null model for the consensus matrix, as outlined in (Humphries et al, 2021).…”

Section: Clustering Comparisonmentioning

confidence: 99%

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Fast-local and slow-global neural ensembles in the mouse brain

Delaney

O’Donnell

2022

Preprint

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Ensembles of neurons are thought to be co-active when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast sub-second timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales each ensemble was mostly contained within a single brain region, whereas at slower timescales ensembles spanned multiple brain regions. These results suggest that the mouse brain performs fast-local and slow-global computations in parallel.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Ensemble Detection Analysismentioning

confidence: 99%

“…A common choice for P in community detection is the 'configuration model' (Fosdick et al, 2018;Humphries et al, 2021).…”

Section: Ensemble Detection Analysismentioning

confidence: 99%

“…clustering method that uses an explicit null model for the consensus matrix, as outlined in (Humphries et al, 2021).…”

Section: Clustering Comparisonmentioning

confidence: 99%

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Fast-local and slow-global neural ensembles in the mouse brain

Delaney

O’Donnell

2022

Preprint

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show abstract

Fast-local and slow-global neural ensembles in the mouse brain

Delaney

O’Donnell

2023

Network Neuroscience

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Ensembles of neurons are thought to be co-active when participating in brain computations. However, it is unclear what principles determine whether an ensemble remains localised within a single brain region, or spans multiple brain regions. To address this, we analysed electrophysiological neural population data from hundreds of neurons recorded simultaneously across nine brain regions in awake mice. At fast sub-second timescales, spike count correlations between pairs of neurons in the same brain region were stronger than for pairs of neurons spread across different brain regions. In contrast at slower timescales, within- and between-region spike count correlations were similar. Correlations between high-firing-rate neuron pairs showed a stronger dependence on timescale than low-firing-rate neuron pairs. We applied an ensemble detection algorithm to the neural correlation data and found that at fast timescales, most ensembles had a high proportion of neurons from a single brain region, whereas at slower timescales ensembles contained roughly equal numbers of neurons from multiple brain regions. These results suggest that the mouse brain may perform fast-local and slow-global computations in parallel.

show abstract

Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models

Cited by 2 publications

References 44 publications

Fast-local and slow-global neural ensembles in the mouse brain

Fast-local and slow-global neural ensembles in the mouse brain

Fast-local and slow-global neural ensembles in the mouse brain

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