The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Detecting Action Potentials in Neuronal Populations with Calcium Imaging
Open accessAbstract
The study of neural circuits requires methods for simultaneously recording the activity of populations of neurons. Here, using calcium imaging of neocortical brain slices we take advantage of the ubiquitous distribution of calcium channels in neurons to develop a method to reconstruct the action potentials occurring in a population of neurons. Combining calcium imaging with whole-cell or perforated patch recordings from neurons loaded with acetoxymethyl ester or potassium salt forms of calcium indicators, we demonstrate that each action potential produces a stereotyped calcium transient in the somata of pyramidal neurons. These signals are detectable without averaging, and the signal-to-noise is sufficient to carry out a reconstruction of the spiking pattern of hundreds of neurons, up to relatively high firing frequencies. This technique could in principle be applied systematically to follow the activity of neuronal populations in vitro and in vivo.
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Self-citationFluorescent Probes of Cell Signaling
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Histed, M. H., Bonin, V., & Reid, R. C. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.016 · Cited by 604
Imaging input and output of neocortical networks in vivo
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Fast nonnegative deconvolution for spike train inference from population calcium imaging
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Self-citationInternal Dynamics Determine the Cortical Response to Thalamic Stimulation
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Metadata
- Authors
- Smetters, D., Majewska, A., & Yuste, R.
- Venue
- Methods
- Publisher
- Elsevier BV
- Published
- 1999-06
- Volume
- 18
- Issue
- 2
- Pages
- 215-221
- ISSN
- 1046-2023 (ISSN-L)
- OpenAlex
- W1994617181
- Cited by
- 317
- References
- 26
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
