Multi-Class and Multi-Label Classification Using Associative Pulsing Neural Networks

This paper introduces the use of a new model of associative pulsing neurons (APN) for multi-class and multi-label classification tasks which are usually performed by separate artificial neural networks. The presented associative pulsing neurons have similar capabilities as various spiking models of neurons, but they additionally have built-in conditional plastic mechanisms which allow creating a neural structure with any given training dataset. Associative pulsing neurons can be connected and adapted very quickly. They have been implemented in the described research to define static patterns and their relations. They have been successfully used to automatically construct associative pulsing neural networks (APNN) to provide a classification of some well-known benchmark training data. These networks use special receptors which transform external stimuli into their internal representation of pulses. Receptors charge connected neurons in different periods of time according to the similarity of the presented input value to the values that they represent. This paper also presents the answer to one of the most challenging tasks in neuroscience, i.e. whether neurons communicate by a rate of pulses or temporal differences between pulses, and how the frequency of pulses influences the neural network activations.