Nov 9, 2018

The DeepTune framework for modeling and characterizing neurons in visual cortex area V4

BioRxiv : the Preprint Server for Biology
Reza Abbasi-AslBin Yu

Abstract

Deep neural network models have recently been shown to be effective in predicting single neuron responses in primate visual cortex areas V4. Despite their high predictive accuracy, these models are generally difficult to interpret. This limits their applicability in characterizing V4 neuron function. Here, we propose the DeepTune framework as a way to elicit interpretations of deep neural network-based models of single neurons in area V4. V4 is a midtier visual cortical area in the ventral visual pathway. Its functional role is not yet well understood. Using a dataset of recordings of 71 V4 neurons stimulated with thousands of static natural images, we build an ensemble of 18 neural network-based models per neuron that accurately predict its response given a stimulus image. To interpret and visualize these models, we use a stability criterion to form optimal stimuli (DeepTune images) by pooling the 18 models together. These DeepTune images not only confirm previous findings on the presence of diverse shape and texture tuning in area V4, but also provide rich, concrete and naturalistic characterization of receptive fields of individual V4 neurons. The population analysis of DeepTune images for 71 neurons reveals how different ty...Continue Reading

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Mentioned in this Paper

Body Regions
Visual System
Biological Neural Networks
Neurons
Disorder of Visual Pathways
Lead Site V4
Neural Network Simulation
Structure of Cortex of Kidney
Primates
Entire Brodmann Areas 17 (Striate Cortex),18 (Parastriate Cortex) and 19 (Peristriate Cortex) of Occipital Lobe

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