Transistor first reported as "little brain cell"
biodigitaljazz.net
biodigitaljazz.net
Edit: An article about the origin of the term and historic connections/comparisons being made between brains and computers: https://www.nzz.ch/digital/computer-wie-die-elektronenhirne-... (in German)
The analogy between the telegraph and the nervous system goes back much further. In 1855, William F. Channing wrote:
> The electric telegraph is thus the nervous system of this nation and of modern society by no figure of speech, by no distant analogy. Its wires spread like nerves over the surface of the land, interlinking distant parts, and making possible a perpetually higher co-operation among men, and higher social forms than have hitherto existed.
The fact that the nervous system involved electricity in some way, was understood by that time. He was probably not the first to think of the analogy, and it would become common by the late 19th century.
[1] William F Channing, "The American Fire-Alarm Telegraph" https://collections.nlm.nih.gov/catalog/nlm:nlmuid-101620765...
It’s not clear to me how far back the computer-as-brain analogy goes, but as far back as 1833 you have Babbage’s difference engine being described as a “thinking machine” https://en.wikipedia.org/wiki/Difference_engine
It is unknown nowadays.
It's like comparing a light switch to an M3 SoC.
In my opinion, that's the interesting axis of comparison (the i/p ability w.r.t. data I care about). I don't particularly care that my transistors can't reproduce or repair themselves from an embedded copy of 'the entire human tech-tree, so far'.
> 3 orders of magnitude the size or more and 3d to boot
To be fair, transistors are orders of magnitude faster in time than neurons.
> It's like comparing a light switch to an M3 SoC.
Bird to a plane is the analogy I see a lot around here. It feels apt.
They are insanely complicated. While the analogy of light switch compared to an M3 isn’t exactly accurate it does a good job of signaling that neurons and transistors are very much different thjngs.
The morphology of neurons is involved in information processing, I don't doubt that at all. It's also involved in staying alive, and staying healthy, and 'being part of the organism', and a great many other things.
It is not at all clear which specific aspects of the morphology are 'for' processing information we care about in the context of intelligence (because again, even info processing can be 'for' things like making sure you breathe reliably).
If we look at the other side, the replication (giant fab facilities) and energy production/management (power lines, PSUs, etc) are separate from the information processing in a computer chip. But, if we consider all of the pieces involved in power generation and replication as part of the chip (just like a brain has), then the complexity of the brain seems loosely equal.
So the more appropriate unit to compare to CPUs and GPUs is synapse number, not neuron number.
Here is an open version of my Annual Review of Neuroscience article with Karl Herrup on the control of neuron number. Old paper but it is one of the first publications to give an accurate estimate of neuron numbers in the human brain right:
But going from a tiny amplifier large digital systems makes you think: what if we don’t go that direction? It’s so embedded into our thinking of silicon, makes me ponder what other base structures we can do and what other algebras might be more useful today.
With, each, more than 60 different types of neurotransmitters, exocytosis, endocytosis, transporters, destructive enzymes, pre- and postsynaptic receptors, ~7000 connections to other neurons and much more I did not think of, even a single neuron is an unbelievably complex machine all by itself.
I think the bigger difference is that transistors and neurons are not actually analogous. They operate in completely different ways, using completely different "encodings".
For now, that kind of fan-in/fan-out has to be simulated sequentially.
https://www.scientificamerican.com/article/100-trillion-conn...
However I think we are getting somewhere. We’re getting to the point where artificial neural networks can rival the complexity of simple invertebrates.
Meanwhile a brain cell is still a lot more complicated and has a place in an entire network of cells and the connections between them that cannot be described by the comparatively simpler equations one can use for a transistor.
Thus, my complaint of trying the (IMO) lazy approach of “the smallest unit of the computer — the transistor - will be equivalent to the smallest unit of the brain - the neuron”.
Just one of the ways visual information is heavily processed before traveling along your optic nerve.
My PI said that a still faulty, but better, analogy is that processing in a neural mass is more akin to the ringing of a bell. It's about waves flowing through a medium.
These ReRAM products did not seem to emerge however.