Protocol for dissecting cascade computational components in neural networks of a visual system

Shanshan Jia; Jian K. Liu; Zhaofei Yu

doi:10.1016/j.xpro.2023.102722

Protocol for dissecting cascade computational components in neural networks of a visual system

Shanshan Jia, Jian K. Liu, Zhaofei Yu^*

^*Corresponding author for this work

Computer Science

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Abstract

Finding the complete functional circuits of neurons is a challenging problem in brain research. Here, we present a protocol, based on visual stimuli and spikes, for obtaining the complete circuit of recorded neurons using spike-triggered nonnegative matrix factorization. We describe steps for data preprocessing, inferring the spatial receptive field of the subunits, and analyzing the module matrix. This approach identifies computational components of the feedforward network of retinal ganglion cells and dissects the network structure based on natural image stimuli.

For complete details on the use and execution of this protocol, please refer to Jia et al. (2021).1

Original language	English
Article number	102722
Number of pages	10
Journal	STAR Protocols
Volume	4
Issue number	4
Early online date	17 Nov 2023
DOIs	https://doi.org/10.1016/j.xpro.2023.102722
Publication status	Published - 15 Dec 2023

Bibliographical note

Acknowledgments:
This work was supported by National Natural Science Foundation of China grants 62176003 and 62088102 (Z.Y.) and Royal Society Newton Advanced Fellowship of UK grant NAF-R1-191082 (J.K.L.).

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Cite this

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abstract = "Finding the complete functional circuits of neurons is a challenging problem in brain research. Here, we present a protocol, based on visual stimuli and spikes, for obtaining the complete circuit of recorded neurons using spike-triggered nonnegative matrix factorization. We describe steps for data preprocessing, inferring the spatial receptive field of the subunits, and analyzing the module matrix. This approach identifies computational components of the feedforward network of retinal ganglion cells and dissects the network structure based on natural image stimuli.For complete details on the use and execution of this protocol, please refer to Jia et al. (2021).1",

author = "Shanshan Jia and Liu, {Jian K.} and Zhaofei Yu",

note = "Acknowledgments: This work was supported by National Natural Science Foundation of China grants 62176003 and 62088102 (Z.Y.) and Royal Society Newton Advanced Fellowship of UK grant NAF-R1-191082 (J.K.L.).",

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AU - Jia, Shanshan

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AU - Yu, Zhaofei

N1 - Acknowledgments: This work was supported by National Natural Science Foundation of China grants 62176003 and 62088102 (Z.Y.) and Royal Society Newton Advanced Fellowship of UK grant NAF-R1-191082 (J.K.L.).

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N2 - Finding the complete functional circuits of neurons is a challenging problem in brain research. Here, we present a protocol, based on visual stimuli and spikes, for obtaining the complete circuit of recorded neurons using spike-triggered nonnegative matrix factorization. We describe steps for data preprocessing, inferring the spatial receptive field of the subunits, and analyzing the module matrix. This approach identifies computational components of the feedforward network of retinal ganglion cells and dissects the network structure based on natural image stimuli.For complete details on the use and execution of this protocol, please refer to Jia et al. (2021).1

AB - Finding the complete functional circuits of neurons is a challenging problem in brain research. Here, we present a protocol, based on visual stimuli and spikes, for obtaining the complete circuit of recorded neurons using spike-triggered nonnegative matrix factorization. We describe steps for data preprocessing, inferring the spatial receptive field of the subunits, and analyzing the module matrix. This approach identifies computational components of the feedforward network of retinal ganglion cells and dissects the network structure based on natural image stimuli.For complete details on the use and execution of this protocol, please refer to Jia et al. (2021).1

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Protocol for dissecting cascade computational components in neural networks of a visual system

Abstract

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