Comment by suddenlybananas
4 days ago
People really overstate the relationship between ANNs and the brain, they have very different mechanisms and only have a similarity if you squint at 100000 feet. ANNs don't have neurotransmitters or even action potentials.
While I agree with this statement, one could argue that what's important is not much the support but the emergent properties - in the same way that a wave is still a wave whether it is in the water or in the air.
Thus putting things similar to neurons in a network and making them able to learn could create behaviors similar to the brain. The fact that that biology used chemistry + electrical signal and computers use ReLU-like activation could be merely choosing the most efficient way to enable training.
Correct. Worst of all, even if you do build a spiking neural network, the update rule is kind of a mystery. To have a good update rule, a biological neuron needs to be kind of like a tiny computer in its own right. You might be able to model synapses as weights between neurons, but the neuron carries further internal states within in itself and how the "update rule" uses those internal states is not known at all.
But neurotransmitters and action potentials don't help in modeling symbolic structure.
That is, yes, ANNs are not brains. There are countless differences. But are there differences at the computational level? ANNs are meant to model brain computation, not brain biology.
(There is still a lot to debate there, I'm not saying "ANNs are perfect computational models for the brain")
>But neurotransmitters and action potentials don't help in modeling symbolic structure.
This is exactly why Fodor argues that psychology should be explained on its on level with symbols rather than appealing to neurology. But if you're interested in modeling symbols, there's much better options than ANNs (see nearly any programming language ever).
>ANNs are meant to model brain computation
But we don't really know how that works! So if you know if you're not modelling the low-level behaviour right, you can't assume that there's a correspondence of the higher level computation when you don't really know what that higher level computations are.
We don't know all the details about how the brain computes, you are right.
But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model.
Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto something all along (I say that grudgingly).
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