[jtbayly]

Ummm.... no. You might be able to make the case that a neuron can be simulated with a very complex set of if-then statements, but that assumes that we can know everything about the state of a dozen inputs, which we can't. At any rate, the brain is not a computer: https://aeon.co/essays/your-brain-does-not-process-informati...

[xj9]

i like that this essay puts the complexity of the brain into perspective, but the distinction between a computer and an organism is kind of arbitrary. brains are physical systems that can be modeled mathematically. if you can create a mathematical model of a thing, you can execute that model on a computer. a brain isn't a classical computer, but that doesn't mean that a computer can't simulate a brain.

we currently lack the ability to recreate a brain-like entity, but the subtext that i am reading here is that the complexity of the brain is such that accurately modelling a brain in mathematical terms is impossible. the "brain-as-computer" model may not be accurate, but everything that exists can be expressed in mathematical (and therefore compute-able) terms.

i doubt that cyberbrains will run on anything that we recognize as a general-purpose cpu. gpu micro-architecture is already a significantly more efficient option for performing nn computations. as our grasp on this stuff improves, more specific silicon is being developed to make it even more efficient.

[jtbayly]

I don't think it's arbitrary. You even distinguished in this comment between a brain and a simulation of a brain. Steel is a physical system that can be modeled mathematically. Accurately simulating steel doesn't make steel.

[a_crc]

Agreed. I think radioactive decay is an even better example of a well modeled physical system that defies simulation. A simulation of an ounce of decaying uranium won't tell you which atoms will decay in which order in a physical chunk. Ergo, somethings defy simulation.

[vntok]

output = if (input > locally_stored_threshold) input * locally_stored_weight else 0

This is not very complex and accurately models neurotransmission. What's missing here is the vastness of connections from a particular neuron towards thousands or more other neurons, but the inherent function is definitely not complex.

* weight being either 1 if you adopt a continous modelization (multiple serial input provide multiple serial output) or a float if you prefer the discrete modelization (sum of input to sum of output)