DeepAI AI Chat
Log In Sign Up

Artificial Neuron Modelling Based on Wave Shape

by   Kieran Greer, et al.

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.


page 1

page 2

page 3

page 4


A New Oscillating-Error Technique for Classifiers

This paper describes a new method for reducing the error in a classifier...

Finding the Shape of Lacunae of the Wave Equation Using Artificial Neural Networks

We apply a fully connected neural network to determine the shape of the ...

The Compact Support Neural Network

Neural networks are popular and useful in many fields, but they have the...

Crushing the Wave – new Z-Wave vulnerabilities exposed

This paper describes two denial of service attacks against the Z-Wave pr...

New Ideas for Brain Modelling

This paper describes some biologically-inspired processes that could be ...

Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina

During hurricane seasons, emergency managers and other decision makers n...

A Single-Pass Classifier for Categorical Data

This paper describes a new method for classifying a dataset that partiti...