Interpretive models use computational and information-theoretic principles to explore the behavioral and cognitive significance of various aspects of nervous system function, addressing the question of why nervous system operate as they do.

Interpretive Models of Receptive Fields

Efficient Coding Hypothesis:

Suppose the goal is to represent images as faithfully and efficiently as possible using neurons with receptive \(RF_1\), \(RF_1\), etc.

Given Image \(I\), we can reconstruct \(I\) using neural responses \(r_{1}\), \(r_{2}\), ..:

Idea is what are the \(RF_{i}\) that minimize the total squared pixelwise errors between \(I\) and \(\hat{I}\) and are as independent as possible?

One can start out with random \(RF_{i}\) and run efficient coding algorithm on natural image patches__, you can use Sparse Coding, ICA (independent component analysis) or predictive coding. The brain may be trying to find _faithful and efficient representation of an animal’s natural environment.