Artificial Neuron Modelling Based on Wave Shape

TL;DR

This paper introduces a novel artificial neuron model that emphasizes matching wave-like input and output shapes, aiming to improve output accuracy through shape resonance and synapse construction.

Contribution

It presents a new neuron processing unit that focuses on wave shape matching, differing from traditional models by incorporating resonance and shape-based learning.

Findings

Shape matching improves output accuracy

Resonance mechanisms facilitate reinforcement learning

Potential for enhanced synapse modeling

Abstract

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.