Topic: natural scene statistics

Our visual system continually adapts to its sensory environment. As a result, both the encoding accuracy of sensory information and perceptual behavior change according to the recent history of sensory input. Here we tested the hypothesis that adaptation to the recent history of sensory input optimally prepares the perceptual system for the future, i.e. establishes efficient sensory representations for the next expected sensory input. We first quantitatively characterized the changes in neural encoding accuracy induced by adaptation with a psychophysical discrimination experiment. We measu

We propose a novel method to predict contextual modulation of local field potential (LFP) gamma synchronization and multi-unit activity (MUA) in a single framework. Convolutional neural networks are state-of-the-art models of the primary visual cortex (V1). Sensory processing in V1 is determined by contextual modulation via feedback and recurrent connections. However, the computational goal and the neural implementation that shape V1 activity are not yet fully understood. We use transfer learning to use an object recognition convolutional neural network (VGG-16) to predict firing rates and gam