Efficient coding of natural scenes improves neural system identification.

ABSTRACT: Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an a

SUBMITTER: Qiu Y 

PROVIDER: S-EPMC10159360 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature