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{
"n": null,
"doi": "10.3389/fnsys.2016.00011",
"claim": "Stimuli Reduce the Dimensionality of Cortical Activity",
"cite_key": "Mazzucato2016",
"evidence": "The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture.",
"effect_size": "qualitative",
"text_access": "fulltext",
"study_system": "computational network model",
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"claim_source_sentence": "We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity.",
"source_provenance_status": "ok",
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"effect_size_source_sentence": "We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity."
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"paper:paper-a87dcbd136f5"
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The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture.