NIPS 2017 - presentations from the Neuroscience session

• Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System • Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit • Quantifying how much sensory information in a neural code is relevant for behavior • Learning to See Physics via Visual De-animation • Shape and Material from Sound • Deep Hyperalignment • Fast amortized inference of neural activity from calcium imaging data with variational autoencoders • Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays • Targeting EEG/LFP Synchrony with Neural Nets • Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons