I\'m going to be forward and say that I\'m not a biologist. I don\'t claim to fu

Question

I'm going to be forward and say that I'm not a biologist. I don't claim to fully understand the functionality of a neuron from an electrical/chemical perspective... I'm curiously gazing from the outside in as a computer programmer.

Now, the big new thing these days with artificial neural networks is simulating them with biological precision- that is, with spiking, actually placing the network in Euclidean space, and all sorts of other goodies (Check out the Human Brain Project; it's really cool. Our own brains are computable, given a powerful enough computer. Probably has a lot of fascinating and horrifying philosophical implications too.), but this isn't the only model that people have commonly used for neural networks.

In the past, multilayer perceptrons were the big thing; I'm not going to go deep into explaining them, but essentially, each neuron simply performs a mathematical function on its inputs and then outputs it. There's no spiking in the sense similar to our neurons. A neuron could be blasting its output at nearly 100% indefinitely and there isn't any objective difference between that and it being at 0% or anywhere else.

Now, I sort of went off on a tangent, but what I'm trying to understand is- why this behavior is not observed in the system of computation that evolution has produced over the many, many years? Why do we use spiking neurons instead? We have instead developed a strange system where spikes travel down chains of neurons in patterns we don't really understand yet to produce the behavior necessary for the creature to survive. My question is, why has this been favored over something similar to multilayer perceptrons? Is it more energy efficient to use a spiking model? The brain does use a disproportionate amount of energy. I wonder if it is more effective as a model for computation.

Explanation / Answer

One of the many advantage of an all-or-none system is that resources can be conserved for timing events that require synchronized collaboration between many cells (like locomotion). Binary behavior may also partially be a side effect of speed and efficient long-distance information transfer (which is one of the great advantages of neurons as cells in the first place).

It should be noted, however, that neurons aren't universally binary. It is more accurate to say that neurons have binary properties that allow neurons to behave in a binary manner (but not all neurons do). On top of that, many continuous (i.e. non-binary) processes underlie the neuron's excitability threshold.

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