Algebraic approach for subspace decomposition and clustering of neural activity

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 clu...

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Bibliographic Details
Main Authors: Adam, Elie M, Sur, Mriganka
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:English
Published: Elsevier BV 2023
Online Access:https://hdl.handle.net/1721.1/150376
Description
Summary: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.