A neural network trained for prediction mimics diverse features of biological neurons and perception

William Lotter, Gabriel Kreiman, David D. Cox

Abstract: The deep convolutional recurrent neural network ‘PredNet’ can be trained to predict future video frames in a self-supervised manner. A surprising result is that it captures a wide array of phenomena observed in natural neuronal systems, ranging from low-level visual cortical neuron response properties to high-level perceptual illusions, hinting at potential similarities between recurrent predictive neural network models and computations in the brain.