Nonlinear Approaches for Neural Encoding and Decoding

Understanding the mapping between stimulus, behavior, and neural responses is vital for understanding sensory, motor, and general neural processing. We can examine this relationship through the complementary methods of encoding (predicting neural responses given the stimulus) and decoding (reconstructing the stimulus given the neural responses). The work presented in this thesis proposes, evaluates, and analyzes several nonlinear approaches for encoding and decoding that leverage recent advances in machine learning to achieve better accuracy. We first present and analyze a recurrent neural network encoding model to predict retinal ganglion cell responses to natural scenes, followed by a decoding approach that uses neural networks for approximate Bayesian decoding of natural images from these retinal cells. Finally, we present a probabilistic framework to distill behavioral videos into useful low-dimensional variables and to decode this behavior from neural activity.