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.