Neural System Identification with Spike-triggered Non-negative Matrix Factorization

Shanshan Jia, Zhaofei Yu, Arno Onken, Yonhong Tian, Tiejun Huang, Jian Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required that can decipher these components in a systematic manner. Recently a method termed 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 the retinal ganglion cell as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells, 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 ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Cybernetics
Early online date5 Jan 2021
DOIs
Publication statusE-pub ahead of print - 5 Jan 2021

Keywords

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

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