Fast neural network adaptation with associative pulsing neurons

Abstract

This paper presents a fast self-organization of neural network structure using a new simplified pulsing model of neurons. These neurons incorporate the concept of time while simplifying many functional aspects of spiking models. This model is attractive because it is computationally very efficient. It allows for fast association of experimental data using conditional plasticity rules built-in neurons. It can be used for the representation of sequential and non-sequential data in neural network architectures. It also allows for the creation of synaptic connections that represent similarity, sequence, proximity, or defining dependencies between data and objects. Thus, this model can be used to develop complex neural graph structures for knowledge representation and retrieval. Such neural structures can be further used for fast search of related data or objects, clustering, classification, recognition, data mining, knowledge exploration, data retrieval, as well as for various cognitive tasks.