KAN (training a neural network by adjusting neuron thresholds instead of
synaptic weights) is not new. The brain does both. Neuron fatigue is the
reason that we sense light and sound intensity and perception in general on
a logarithmic scale. In artificial neural networks we model this by giving
each neuron an extra weight with a fixed input.

All neural networks are trained by some variation of adjusting anything
that is adjustable in the direction that reduces error. The problem with
KAN alone is you have a lot fewer parameters to adjust, so you need a lot
more neurons to represent the same function space. That's even with 2
parameters per neuron, threshold level and steepness. The human brain has
another 7000 parameters per neuron in the synaptic weights.

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Artificial General Intelligence List: AGI
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