Topic: natural input statistics

Neural system identification aims at learning the response function of neurons to arbitrary stimuli by incorporating the right assumptions into the model that facilitate generalization beyond the particular stimuli used during training [1]. Here, we present normative network regularization as a novel regularization tool that allows to flexibly impose prior assumptions into the model training. In particular, we use this approach to incorporate the efficient coding hypothesis as a regularizer which states that neural response properties of sensory representations are strongly shaped by the need