Riddled Basins of Attraction in Systems Exhibiting Extreme Events · HackerPapers discussions

Riddled Basins of Attraction in Systems Exhibiting Extreme Events — mentioned by nyrikki

Sun, 04 Aug 2024 20:24:59 GMT

If the goal was to create an artificial neural network that better approximated the biological human brain, yes the perceptron model is insufficient. If your goal is to produce a useful model on real hardware and it works...no Remember the constraints of ANNs being universal approximaters (in theory) 1) The function you are learning needs to be continuous 2) Your model is over a closed, bounded subset of R^n 3) The activation function is bounded and monodial Obviously that is the theoretical UAT constraints. For gradient decent typically used in real ML models, the constraint of finding only…

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Riddled Basins of Attraction in Systems Exhibiting Extreme Events — mentioned by nyrikki

Mon, 08 Jul 2024 19:17:23 GMT

dendrocentric compartmentalization, spike timing, bandpass in the dendrites, spike retiming etc... aren't covered in the above. But it is probably important to define 'computable' Typically that means being able that can take a number position as input and output the digit in that location. So if f(x) = pi, f(3) would return 4 Even the real numbers are uncomputable 'almost everywhere', meaning choose almost any real number, and no algorithm exists to produce it as f(x) Add in ion channels and neurotransmitters and continuous input and you run into indeterminate features like riddled basins…

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Riddled Basins of Attraction in Systems Exhibiting Extreme Events — mentioned by nyrikki

Mon, 22 Apr 2024 19:50:38 GMT

It is more than a wild guess, it is a guess based on the outdated perceptron model of the brain. Active dendrites the that can do operations like xor before anything reaches the soma, or use spike timing, once again before the soma are a couple of examples. SNNs, or spikey artificial NNs have been hitting limits of the computable numbers. Riddled basins as an example, which are sets with no open subsets. Basically any circle you can draw will always contain at least one point on a boundary set. https://arxiv.org/abs/1711.02160 This is a much stronger constraint than even classic chaos. While…

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Riddled Basins of Attraction in Systems Exhibiting Extreme Events — mentioned by nyrikki

Sun, 18 Feb 2024 20:48:20 GMT

same thing with any feed forward network too. They are all piece-wise linear in respect to inputs. Layers reduce resource requirements and make some patterns easier or even practical to find, but any ANN that is a FNN supervised learning could be represented as a parametric linear regression. Unsupervised learning, that tends to use clustering is harder to visualize but is the same thing. You still have ANNs, which have binary output, which can be viewed through the lens of deciders. They have to have unique successor and predecessor functions. Really this is just set shattering that relates…

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