Artificial Neuron Modelling Based on Wave Shape
This work addresses a potential bottleneck in neural network design for researchers in computational neuroscience and AI, but appears incremental as it builds on traditional feedforward networks with a shape-matching twist.
The paper tackles the problem of improving artificial neural network processing by introducing a neuron model that matches wave-like shapes between input and output, aiming to facilitate easier transformation into desired output values through reinforcement via resonance and synapse construction.
This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like 'shape' of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.