Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques-projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)-provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are...

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Bibliographic Details
Main Authors:	Lee, T.S (Author), Rockwell, H. (Author), Tang, S. (Author), Wu, Z. (Author), Zhang, Y. (Author)
Format:	Article
Language:	English
Published:	Public Library of Science 2021
Subjects:	algorithm Algorithms animal animal cell animal experiment Animals Article biological model biology calcium signaling CLXXVI-3C11-F14 cell line Computational Biology controlled study convolution algorithm convolutional matching pursuit regression convolutional neural network convolutional projection pursuit regression kernel method Macaca mathematical computing measurement accuracy Models, Neurological nerve cell Neural Networks, Computer Neurons noise nonhuman orientation physiology prediction receptive field rhesus monkey visual cortex Visual Cortex visual stimulation
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Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

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