Paper title: Disinhibitory signaling enables flexible coding of top-down information in cortical networks

(should be qualified as in-silico visual systems)

Method: replicate fMRI findings of visual abstraction using simple networks to model what's essential

Gist: in tasks 'Inhibitory neurons that suppress other inhibitory neurons seem to pass key information from the “thinking” part of the system to the “sensing” component of the system'

I've heard the same for motor control: it's not that the cortex aims for one action; it aims for a bunch, but most are inhibited. (You see this in chaotic movement when inhibition fails).

So it's not really "think and see" but "what you see when you're doing a task".

(There's some analogy in there wrt (AI) exuberance effacing selectivity in investment decisions...)

Independent of the research itself, the article makes it seem as if neuroscientist are just discovering the deep recursion all the way back to V1. The idea that this was a one way stream of information processing was discarded a long time ago. Those back projections probably serve lots of functions, but we can be pretty sure they are there to let current context bias the weights for quicker recognition and reaction...e.g. if your context includes snakes, your visual system will attune to recognizing snakes even faster.

Reverse engineering how algorithms in the brain work is a really promising path towards making genuine AI systems which would make the current crop of LLMs obsolete.