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I'd be interested to learn how many biological neurons it takes to approximate one artificial neuron. Mathematically, over some finite interval, approximating
by coffeecat 5y ago
I'd be interested to learn how many biological neurons it takes to approximate one artificial neuron.
Mathematically, over some finite interval, approximating y=sin(x) using a Taylor series, or approximating y=x**n using a Fourier series, can both require a large number of terms. But this doesn't imply that a sine/cosine function is more computationally complex than a monomial, or vice versa.
- PeterisP 5y agoThe article asserts that on average they need ~1000 "neurons" to approximate one biological neuron. In the opposite direction, assuming we had manipulation techniques create new neurons, position them and stretch out axons arbitrarily and control synapse linking strength (which we can't now), then IMHO there could be a 1-to-1 correspondence; the same octopus neurons that McCulloch-Pitts model tried to describe could also each approximate one artificial neuron for inference. Of course, learning is a different issue, brains don't exactly do back-propagation and gradient descent.
- diskzero 5y agoIs there any correlation between a perceptron and a neuron? When I first encountered the concept of a perceptron, I was told Minsky and Papert modeled the perceptron on the biology of a neuron. Is this correct? If so, how has this initial model stood up and we learn more about the physical structure of the neuron.
- selfish_gene 5y agoThe answer is 1. You only need to put all your synapses at the soma and voila