Great paper. I'm a bit confused about the novelty of the XOR result though. As I understand it, Yiota's model from 2003 (where a pyramidal cell was equivalent to a 2-layer network) in https://www.cell.com/neuron/fulltext/S0896-6273(03)00149-1 … would also be able to solve the XOR problem.
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:) But this is *real* stuff, real neurons, real currents and real spikes, so nobody can complain that Albert "simply made up" something in a model by tuning parameters etc :)
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Super Interesting! I haven't had a chance to read this yet, but what's the evidence that this is about human layer 2/3 neurons in particular?
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During the last 2 decades there has been quite some work on rodent layer 2/3 neurons with similar techniques. But in principle it is of course possible that in some other species (primates) this phenomenon will one day be observed. So far it hasn't been.
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I have an idea what this means, but could someone please explain this in more layman terms?
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1/ The XOR function (given two predicates A and B, XOR(A,B) = T if A=T and B=F, or vice versa) has been a focus for computational neuroscience since Minsky and Papert showed that a single linear perceptron was incapable of learning it.
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This looks terrific, except for the paywall..
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Makes me miss Aaron Swartz
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Maybe there is hope for the perceptrons yet.
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Single neurons already have multiple inputs and outputs… they’re probably more like a multilayered perceptron on their own And I bet there are other neuron types that specialize in convolutions and permutations and other “primitives” operations that im uneducated about
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