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

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journal contribution
posted on 28.01.2021, 09:20 by Shanshan Jia, Zhaofei Yu, Arno Onken, Yonghong Tian, Tiejun Huang, Jian K Liu
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.

History

Author affiliation

Department of Neuroscience, Psychology and Behaviour, College of Life Sciences

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cybernetics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2168-2267

eissn

2168-2275

Copyright date

2021

Available date

05/01/2021

Language

en