DOI: 10.1101/201764Nov 5, 2018Paper

Deep convolutional models improve predictions of macaque V1 responses to natural images

BioRxiv : the Preprint Server for Biology
Santiago A CadenaAlexander S. Ecker

Abstract

Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have been successfully applied to neural data: On the one hand, transfer learning from networks trained on object recognition worked remarkably well for predicting neural responses in higher areas of the primate ventral stream, but has not yet been used to model spiking activity in early stages such as V1. On the other hand, data-driven models have been used to predict neural responses in the early visual system (retina and V1) of mice, but not primates. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. Even though V1 is rather at an early to intermediate stage of the visual system, we found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space...Continue Reading

Citations

Jun 6, 2018·Journal of Computational Neuroscience·Yimeng ZhangShiming Tang
Dec 12, 2018·Nature Neuroscience·Maxwell H TurnerFred Rieke
Jan 19, 2019·Nature Communications·Warrick RoseboomAnil K Seth
Apr 27, 2019·Journal of Vision·William F KindelJoel Zylberberg
May 3, 2019·Science·Pouya BashivanJames J DiCarlo

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Jupyter
Matplotlib
DataJoint
Seaborn
Numpy
Scipy
VGG
Adam
Tensorflow
Docker

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