DOI: 10.1101/516484Jan 17, 2019Paper

Evolving super stimuli for real neurons using deep generative networks

BioRxiv : the Preprint Server for Biology
Carlos R. PonceMargaret S Livingstone

Abstract

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.

Related Concepts

Cerebral Cortex
Neurons
Visual Cortex
Anatomical Space Structure
Biological Neural Networks
Neural Network Simulation
Neural Stem Cells
Deep Sequencing

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