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. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T1af6c40307437a26-M6fb2c5e244ff97d1ad88ca92 Delivery options: https://agi.topicbox.com/groups/agi/subscription
