XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization.

ABSTRACT: A longstanding question in sensory neuroscience is what types of stimuli drive neurons to fire. The characterization of effective stimuli has traditionally been based on a combination of intuition, insights from previous studies, and luck. A new method termed XDream (EXtending DeepDream with real-time evolution for activation maximization) combined a generative neural network and a genetic algorithm in a closed loop to create strong stimuli for neurons in the macaque visual cortex. Here we extensively and systematically evaluate the performance of XDream. We use ConvNet units as in silico models of neurons, enabling experiments that would be prohibitive with biological neurons. We evaluated how the method compares to brute-force search, and how well the method generalizes to different neur

SUBMITTER: Xiao W 

PROVIDER: S-EPMC7316361 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature