[Hasu]

A lot of things feel self-evident then turn out to be completely wrong.

We don't understand the processes in the brain well enough to assert that they are doing computation. Or to assert that they aren't!

> say the brain is non-computable is to assert the existence of a soul, in my opinion

I don't believe in souls, but the brain might still be non-computable. There are more than two possibilities.

If it is the case that brains are doing something computable that is compatible with our Turing machines, we still have no idea what that is or how to recreate it, simulate it, or approximate it. So it's not a very helpful axiom.

[throwup238]

> We don't understand the processes in the brain well enough to assert that they are doing computation. Or to assert that they aren't!

We absolutely do know enough about neurons to know that neural networks are doing computation. Individual neurons integrate multiple inputs and produce an output based on those inputs, which is fundamentally a computational process. They also use a binary signaling system based on threshold potentials, analogous to digital computation.

With the right experimental setup, that computation can be quantified and predicted down to the microvolt. The only reason we can't do that with a full brain is the size of the electrodes.

> I don't believe in souls, but the brain might still be non-computable. There are more than two possibilities.

The real issue is neuroplasticity which is almost certainly critical to brain development. The physical hardware the computations are running on adapts and optimizes itself to the computations, for which I'm not sure we have an equivalent.

[nyrikki]

dendrocentric compartmentalization, spike timing, bandpass in the dendrites, spike retiming etc... aren't covered in the above.

But it is probably important to define 'computable'

Typically that means being able that can take a number position as input and output the digit in that location.

So if f(x) = pi, f(3) would return 4

Even the real numbers are uncomputable 'almost everywhere', meaning choose almost any real number, and no algorithm exists to produce it as f(x)

Add in ion channels and neurotransmitters and continuous input and you run into indeterminate features like riddled basins, where even with perfect information and precision and you can't predict what exit basin it is in.

Basically look at the counterexamples to Laplace's demon.

MLPs with at least one hidden layer can approximate within an error bounds with potentially infinite neurons, but it can only produce a countable infinity of outputs, while biological neurons, being continuous input will potentially have an uncountable infinity.

Riddled basins, being sets with no open subsets is another way to think about it.

Here is a paper for that.

https://arxiv.org/abs/1711.02160

[20after4]

We can write code that writes code. Hell even current LLM tech can write code. It's at least conceivable that a artificial neural network could be self-modifying, if it hasn't been done already.