No feedback from cortex. Within the retina yes.
Would you agree the whole point of opsins is to rep. photon count weighted by wavelength?
I want to understand at what point you would say something is a representation. Not sure how to convince.

Or do you prefer the word encode?

Photoreceptors definitely represent photons. To hold off on that judgement until we have a complete theory of the brain seems impractical. Similar arguments could be made for other sensory systems, especially early. Are you thinking mainly of higher order areas?

I am confused. Neither is either, except in a very abstract sense. Is this a counter example?

All digital twins are models, not all models are digital twins. I like the distinction of a model that predicts a real thing, e.g. a specific neuron in a brain. As opposed to a toy model that predicts qualitative features of a class of neurons in general.

Assuming LDS with synapse count as weight, yes, later…

In fly its typically 10-100 inputs to a given neuron. In plot below in degree is number of neurons as inputs. Seems pretty doable. Not sure what mouse is like.

In fly connection probability is 0.00016, I wonder if this goes down with bigger brains b/c geometric constraints?

If you know the non-zero entries of the matrix (connectome) it helps a-lot because vast majority entries are zero: pmc.ncbi.nlm.nih.gov/articles/PMC...
Knowing synapse ‘types’ (from anatomy) could dramatically reduce number of free parameters even more.

Please explain!!!

Why is this considered a problem?

Providing an unavailable dataset e.g., connectome, has led to scientific progress.

Analyzing a model can be more insightful than knowing it is a better fit than a prior model.

Eli5 if you have a hot cold neuron but its sometimes wrong, it is helpful to have a neuron that tells you when its wrong.
Statistically var(x+y)=var(x) + var(y) + cov(x,y) so with negative cov you can get less variance (noise).

Computational models of the brain. Reading from a neurophilosophy undergrad course taught by one of Pat Churchland’s graduate students Brian Keeley. Made the idea of computational models of the brain concrete (and exciting!) for me.