Speaker unverified: belief

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…In in your book you you spoke about a trajectory I suppose of possibly failures of simplification. We just spoke about reflex theory. 1 of the big things is this metaphor of cognition or the brain perhaps as being a kind of computer. Yeah. And you spoke about the early roots of this from reflex theory to cybernetics and computationalism. Can you sketch that out? So the connecting thread is really this idea that what cognition is, is something that is machine like. That what I know going back to the seventeenth century, this is a view associated with the philosopher and physicist and physiologist Rene Descartes who said, we need to give up, we need to go along with this idea that everything that happens in the body is explicable in terms of quite simple mechanistic forces. And this, you know, idea that biological systems are machine like has obviously been hugely influential in the different branches of science. The reflex theory was 1 instance of that. People often said machine like reflexes and making comparisons with sort of Newtonian decomposition. With the computational framework, have an actual machine, a digital or analog computer, which could be compared with brain processes. I mean, cybernetics is an interesting stage along the way because they were buildings of little devices that had some degree of autonomy made up of and and supposed to be emulating versions of negative and positive feedback and as hypothesized to occur in the body. But, yeah, I would say at the core of this research idea is that if what's going on in the body is ultimately a mechanistic process, then by redoing engineering with this non living system which is capturing some of the core operating principles that we find in biology, then we can use that device as a map, as a as a resource to then reinterpret what's going on in the biological system. You saw that with, for example, McCulloch and Pitts in their 1943 sort of landmark paper of interpreting neuronal cells as logic gates and then saying, yeah, you could build a computer out of neural nets. This is the origin of neural nets as we know them today. This is the birth of the idea. But then using that notion that neurons are logic gates to then interpret what's going on in physiology. So what I describe in, and it's in chapter 4 of the book, is this sort of back and forth thing of of making devices which are somewhat inspired by biology, and then using those then as the lens through which to review biology again. And I say that the advantage and the appeal of this process is that it allows you to or gives you kind of license to ignore so many things that are happening in the brain and nervous system which are just not shared with non living machines. Like all of the biochemistry, all of the ways that neural tissue is shaped by vasculature and interacts with the immune system, and all of that sort of background stuff that if you're a theoretical computational neuroscience, you can say, I'm only interested in the computational properties of the brain. I don't need to care about all of that messy biological detail. So it gives you a kind of tunnel vision which a scientist can be fine to have tunnel vision. You can't take in everything at once all of the time. ed to care about all of that messy biological detail. So it gives you a kind of tunnel vision which a scientist can be fine to have tunnel vision. You can't take in everything at once all of the time. But what I take issue with is the kind of ontologization of that saying that because computational neuroscience is this successful field of inquiry, we know now that the brain is a computer. I think that is not an inference we should make.…

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