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Comment by hexaga | Hacker News Reader

It's worth noting that much of the cellular complexity isn't for information processing (insofar as anything can be 'for' something in biological systems). There's all the implicit self-replication stuff, everything you need to tap into energy / physical resources, self-repair, homeostasis, coexistence with other living cells, etc ad nauseum.

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.

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The morphology (cellular shape) of a neuron absolutely is involved in the processing of information. Additionally neurons aren’t just simple on/off units like we pretend they are. They are complicated little computers. They change their shape, gene expression, and electrical sensitivity in response to environmental factors, chemical signals from other neurons, and internal clocks.

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.

Absolutely. And every time someone talks about what biological systems do, the word 'involved' comes into play.

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).

I think the interesting (and scientifically unanswered) question here is: what is the minimum amount of a neuron needed for information processing alone?

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.

It is generally accepted that the locations and molecular structure of synapses are a critical determinant of CNS function. And a typical neuron will have and make hundreds to several thousands synapses.

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:

https://www.nervenet.org/papers/NUMBER_REV_1988.html

Transistors are just amplifiers. But you can arrange several to get a logical NOR gate and Boolean algebra becomes available at a tiny physical scale.

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.

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