As a neuroscientist, I can tell you with high confidence that many if not most neuroscientists would agree with you.

You might also be interested in "Could a Neuroscientist Understand a Microprocessor?"

https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

I can't recommend this paper enough to anyone interested in neuroscience.

It's a thoughtfully executed set of experiments that IMHO should be part of every computational neuroscientist's training.

This paper was thoroughly entertaining. Thanks!

> Neuroscience is so laughably primitive.

Its hard to comprehend how complex of a machinery the brain is when you are "just" a computer scientist. I often see people from the AI field eager to do a short rotation in a neuroscience lab to "learn how the brain works" and then go back to their company and develop a novel algorithm to make a paradigm shift in AI field - it's enthusiastic but laughably primitive.

The technology available to understand this complex machine is primitive when you compare it to electronics or AI tech. This is not because of the lack of funding and talent being invested in the field but because of the highly complex and efficient nature of the brain that has evolved over millions of years but only started to be investigated anatomically for less than a couple of centuries. Arguably, the same problem exists in the AI field too when it comes to understanding the black-box models. We don't even entirely comprehend how the artifical neural network based models work in many use-cases; there are ideas floating around to make sense of it which use the same strategy of "plug in/out and play" to understand what parameter affects what in your model which to neuroscientists seems (yet again) laughably primitive.

And people from neuro come into AI for short rotations pretty regularly to do astoundingly naive work there which amounts to nothing at all.

Maybe both fields could stop flinging shit and realize that they are both missing a key piece of the puzzle...

If you look at almost any complex phenomena that humans didn't actually create, you will see little actual knowledge. Neuroscience, much of biology and economics are examples.

Most areas of the brain are rather homogenous. They've done experiments rewiring inputs going to the visual cortex to the auditive cortex in rodents and they could see. Further, artificial neural nets demonstrate that extremely simple repetitive structures can implement a vast function space simply by training them by stochastic gradient descent. The most complex structures in the brain may be hardwired social behaviors, including facial expression and recognition, but these may not be necessary for the intelligence part as individuals strongly impaired in hardwired social behavior (autists) are sometimes capable of complex thought.

Being a mathematician who's dabbled in neuroscience a bit, I think that's essentially right (though perhaps I wouldn't have said "laughable"). But I also think this reflects the differences between how science and engineering progress -- our ability to exploit a natural phenomenon (or class of phenomena) often outstrips our ability to explain what's going on. As examples, I'd argue that for much of aviation history (and perhaps now too, I don't know), our ability to build flying machines far outstrips our understanding of how birds and insects (or even planes) fly.

I guess neuroscience mostly compares to the condensed matter / semiconductor field, in the computer analogy. They know everything about the transistors but guess at why they're connected like they are. And some in their respective fields will also be very interested in the high-level connections although maybe with less depth..

Unfortunately, in the neuroscience area, there is not yet any analogy to a computer programmer.