This is a question for anyone who knows more (or reads more) about neural networks than me. My problem with the perceptron since I was a kid is that it doesn't seem to model how neurons work very well at all. It creates a neuron that exists in a timeless place; kind of a homo economicus for cognition.
My understanding of neurons: The signals arrive at the dendrites at different times, as soon as a signal arrives its strength decays steadily, and when the sum of decaying signals and an additional signal reach a threshold, the axon fires, getting rid of all energy and beginning the process again (creating the largely irregular pops that everybody is used to listening to.) Aside from that, there are effects from other bodies and chemical concentrations between neurons that are mostly unknown (that I get leaving out as not central to the process.)
My question: is there any paper that explains how that process, which seems pretty easy to model, can be mathematically reduced to perceptrons (hopefully comprehensible to the educated layman) which are just addition and a condition?
My understanding of neurons: The signals arrive at the dendrites at different times, as soon as a signal arrives its strength decays steadily, and when the sum of decaying signals and an additional signal reach a threshold, the axon fires, getting rid of all energy and beginning the process again (creating the largely irregular pops that everybody is used to listening to.) Aside from that, there are effects from other bodies and chemical concentrations between neurons that are mostly unknown (that I get leaving out as not central to the process.)
My question: is there any paper that explains how that process, which seems pretty easy to model, can be mathematically reduced to perceptrons (hopefully comprehensible to the educated layman) which are just addition and a condition?