Protocol for dissecting cascade computational components in neural networks of a visual system
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Updated: Jul 11, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Shanshan Jia1, Jian K Liu2, Zhaofei Yu1
1School of Computer Science, Peking University, Beijing 100871, China; Institute for Artificial Intelligence, Peking University, Beijing 100871, China; Department of Computer Science and Technology, Peking University, Beijing 100871, China.
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.
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