[renewiltord]

Doesn't really matter to modern CS, but Rosenblatt's original perceptron paper is a good read on this. ANNs were specifically inspired by Natural NNs and there were many attempts to build ANNs using models of how the human brain works, specifically down to the neuron.

[dekhn]

I;m sure you know but one of the best ways to get neuro folks worked up is to say anything about neural networks being anything like neurons in brains.

(IMHO, Rosenblatt is an underappreciated genius; he had a working shallow computer vision hardware computer long before people even appreciated what an accomplishment that was. The hardware was fascinating- literally self-turning potentiometer knobs to update weights.

[renewiltord]

If I'm being honest, I do know they get annoyed by that stuff but I've never really understood why. It's a somewhat common pattern in Mathematics as an avenue for hypotheses to take an existing phenomenon, model some subset of its capabilities, use that to define a new class of behaviour, follow that through to conclusions, then use that to go back to seeing if those conclusions apply to the original phenomenon.

A theoretical such thing might be for us to look at, say, human arms and say "Well, this gripping thing is a cool piece of functionality. Let's build an artificial device that does this. But we don't have muscle contraction tech, so we'll put actuators in the gripping portion. All right, we've built an arm. It seems like if we place it in this position it minimizes mechanical wear when not in action and makes it unlikely for initial movement to create undesired results. I wonder if human arms+hands have the same behaviour. Ah, looks like not, but that would have been interesting if it were the case"

Essentially that's just the process of extracting substructure and then seeing if there is a homomorphism (smooshy type abuse here) between two structures as a way to detect yet hidden structure. Category theory is almost all this. I suppose the reason they find it annoying is that there are many mappings that are non-homomorphic and so these are the false cognates of concepts.

Still, I think the whole "An ANN is not a brain" thing is overdone. Of course not. A mechanical arm is not an arm, but they both have response curves, and one can consider a SLAM approach for the former and compare with the proprioceptive view of the latter. It just needs some squinting.

Anyway, considering your familiarity with R and his work, I think I'm not speaking to the uninitiated, but I thought it worth writing anyway.

[lupire]

It's an ego thing and a focus thing. They put so much effort into studying the biological details, that they resent people exteacting value without going to that effort, and feel offended at the implication that what they are studying is simple.

Ultimately it's largely down to misapprehension of the difference between emulating a neuton and simulating a neuron, and defensiveness about an approximate model.

[robwwilliams]

Hmm, in my experience this is not the primary reason, and I would also argue that the original statement is wrong. Computational neuroscientist are very comfortable (too comfortable perhaps) with simplified models of neurons and brain circuits. We accept them as stick figures. DNNs fit into the typical mindset of synaptic and neural circuits. Reading Synaptic Organization of the Brain should leave you stunned at the relative simplicity of how neuroscientists think the brain works.

Most neuroscientists do not indulge in deep thought because it does not pay the bills. NIH does not push us toward “understanding cognition and consciousness”. They push us toward treatments for Alzheimer’s, schizophrenia, stroke, epilepsy, and addiction. We do not have time for the fun stuff until we are ready to retire, which is when many of us become terrible neurophilosophers, e.g., Eccles.