Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization

Shanshan Jia; Zhaofei Yu; Arno Onken; Yonghong Tian; Tiejun Huang; Jian K. Liu

doi:10.1109/TCYB.2020.3042513

Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization

Shanshan Jia, Zhaofei Yu^*, Arno Onken, Yonghong Tian, Tiejun Huang, Jian K. Liu

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.

Original language	English
Pages (from-to)	4772-4783
Number of pages	12
Journal	IEEE Transactions on Cybernetics
Volume	52
Issue number	6
Early online date	5 Jan 2021
DOIs	https://doi.org/10.1109/TCYB.2020.3042513
Publication status	Published - Jun 2022

Bibliographical note

Funding:
This work was supported in part by the National Natural Science Foundation of China under Grant 61806011, Grant 61961130392, Grant 61825101, and Grant U1611461; in part by the National Postdoctoral Program for Innovative Talents under Grant BX20180005; in part by the China Postdoctoral Science Foundation under Grant 2018M630036; in part by the Zhejiang Lab under Grant 2019KC0AB03 and Grant 2019KC0AD02; and in part by the Royal Society Newton Advanced Fellowship under Grant NAF-R1-191082.

Keywords

Neural network
neural spike
nonlinearity
non-negative matrix factorization
receptive field
system identification

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10.1109/TCYB.2020.3042513

https://ieeexplore.ieee.org/document/9314212/

Cite this

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title = "Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization",

abstract = "Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.",

keywords = "Neural network, neural spike, nonlinearity, non-negative matrix factorization, receptive field, system identification",

author = "Shanshan Jia and Zhaofei Yu and Arno Onken and Yonghong Tian and Tiejun Huang and Liu, {Jian K.}",

note = "Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 61806011, Grant 61961130392, Grant 61825101, and Grant U1611461; in part by the National Postdoctoral Program for Innovative Talents under Grant BX20180005; in part by the China Postdoctoral Science Foundation under Grant 2018M630036; in part by the Zhejiang Lab under Grant 2019KC0AB03 and Grant 2019KC0AD02; and in part by the Royal Society Newton Advanced Fellowship under Grant NAF-R1-191082.",

year = "2022",

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doi = "10.1109/TCYB.2020.3042513",

language = "English",

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AU - Jia, Shanshan

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AU - Liu, Jian K.

N1 - Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 61806011, Grant 61961130392, Grant 61825101, and Grant U1611461; in part by the National Postdoctoral Program for Innovative Talents under Grant BX20180005; in part by the China Postdoctoral Science Foundation under Grant 2018M630036; in part by the Zhejiang Lab under Grant 2019KC0AB03 and Grant 2019KC0AD02; and in part by the Royal Society Newton Advanced Fellowship under Grant NAF-R1-191082.

PY - 2022/6

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N2 - Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.

AB - Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.

KW - Neural network

KW - neural spike

KW - nonlinearity

KW - non-negative matrix factorization

KW - receptive field

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DO - 10.1109/TCYB.2020.3042513

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JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

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ER -

Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization

Abstract

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Keywords

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