Algebraic approach for subspace decomposition and clustering of neural activity.
| Autor: | Adam EM; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: eadam@mit.edu., Sur M; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: msur@mit.edu. |
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| Jazyk: | angličtina |
| Zdroj: | STAR protocols [STAR Protoc] 2022 Nov 11; Vol. 3 (4), pp. 101841. Date of Electronic Publication: 2022 Nov 11 (Print Publication: 2022). |
| DOI: | 10.1016/j.xpro.2022.101841 |
| Abstrakt: | We developed an approach to decompose neuronal signals into disjoint components, corresponding to task- or event-based epochs. This protocol describes how to project behavioral templates onto a low-dimensional subspace of neuronal responses to derive neuronal templates, then how to decompose and cluster neuronal responses using these derived templates. We outline these steps on complementary datasets of calcium imaging and spiking activity. Our approach relies on fundamental, linear algebraic principles and is adaptive to the temporal structure of the neural data. For complete details on the use and execution of this protocol, please refer to Adam et al. (2022). 1 . Competing Interests: The authors declare no competing interests. (© 2022 The Author(s).) |
| Databáze: | MEDLINE |
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