[westurner]

So, sensor resolution is higher, there are multiple fields being integrated, in a massively-parallel spreading-activation Biological Neural Network, and that's how blank-slate creatures just know?

Is there enough information content - per the Shannon entropy definition or otherwise - in DNA and/or RNA to code for the survival-selected traits that

I'm not sure that the (Shannon entropy, MIC, Kolmogorov,) information content of the samples is the limit of any given network trained therefrom? Is there anything to be gained from upsampling and adding e.g. gaussian blur (noise)? Maybe it's feature engineering, maybe it's expert methods bias, maybe it's just sensor fusion; that's the magic noise.

[westurner]

Perhaps this is moving the goalposts a bit, but e.g. depixelation does appear to defy such a presumed limit due to apparent information content? Perhaps it is that the network reading the sensor carries additional information associated with the lower-resolution or additional-fields' sensor data?

https://github.com/krantirk/Self-Supervised-photo :

> Given a low-resolution input image, PULSE searches the outputs of a generative model (here, StyleGAN) for high-resolution images that are perceptually realistic and downscale correctly.

Maybe no amount of feature engineering can actually add information?