Oct 11, 2017

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

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


Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict neural responses quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. At the same time, recent advances in machine learning have shown that deep neural networks can learn highly nonlinear functions for visual information processing. Two approaches based on deep learning have recently been successfully applied to neural data: transfer learning for predicting neural activity in higher areas of the primate ventral stream and data-driven models to predict retina and V1 neural activity of mice. However, so far there exists no comparison between the two approaches and neither of them has been used to model the early primate visual system. Here, we test the ability of both approaches to predict neural responses to natural images in V1 of awake monkeys. We found that both deep learning approaches outperformed classical linear- nonlinear and wavelet-based feature representations building on existing V1 encoding theories. On our dataset, transfer learning and data-driven models performed similarly, while the data-driven model employed a much simpler architecture. Thu...Continue Reading

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Mentioned in this Paper

Visual System
Biological Neural Networks
Entire Retina
Retinal Diseases
Nijmegen Breakage Syndrome
Neural Stem Cells

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