ArticlePLoS computational biology2020
Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Disentangling Stimulus & Population Dynamics in Mouse V1: Orthogonal Subspace Decomposition for Neural Representation.bioRxiv : the preprint server for biology · 2025Article
- Disentangling signal and noise in neural responses through generative modeling.PLoS computational biology · 2025Article
- Split genetically encoded calcium indicators for interorganellar junctions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Flexibility of intrinsic neural timescales during distinct behavioral states.Communications biology · 2024Article
- Disentangling signal and noise in neural responses through generative modeling.bioRxiv : the preprint server for biology · 2024Article
- Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions.Current biology : CB · 2023Article
- Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions.bioRxiv : the preprint server for biology · 2023Article
- Dimensionality reduction of calcium-imaged neuronal population activity.Nature computational science · 2023Article
- A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.Nature neuroscience · 2022Article
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Limitations to Estimating Mutual Information in Large Neural Populations.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
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Abstract
The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.
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