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

@article{Jia2021NeuralSI,
  title={Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization.},
  author={Shanshan Jia and Zhaofei Yu and Arno Onken and Yonghong Tian and Tiejun Huang and Jian K. Liu},
  journal={IEEE transactions on cybernetics},
  year={2021},
  volume={PP}
}
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… 
4 Citations
Dissecting cascade computational components in spiking neural networks
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A deep-learning model is used for identifying the computational elements of the retinal circuit that contribute to learning the dynamics of natural scenes and reveals both the shapes and the locations of the spatiotemporal receptive fields of ganglion cells.
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