Evolving spiking neural network—a survey

Abstract

This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We summarize the functioning of the method, discuss several of its extensions and present a number of applications in which the eSNN method was employed. We focus especially on some proposed extensions that allow the processing of spatio-temporal data and for feature and parameter optimisation of eSNN models to achieve better accuracy on classification/prediction problems and to facilitate new knowledge discovery. Finally, some open problems are discussed and future directions highlighted.

MSAFIS: an evolving fuzzy inference system

“…A new concept is addressed in [41] for handling drifts in data streams during the run of online evolving modeling processes in a regression context. The paper [40] provides a comprehensive literature survey on the evolving spiking neural network, while certain methods of spiking neural networks are reviewed in [18] and claimed to be suitable for the creation of a unifying computational framework for learning and understanding of various spatio-and spectro-temporal brain data. In [48], the authors propose a task recommendation framework based on an unified probabilistic matrix factorization, aiming to recommend tasks to workers in dynamic scenarios.…”

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model

“…Thus, the model can be created using low computational complexity and limited memory size. Evolving fuzzy [17] and neuro-fuzzy [18] systems are the most popular approaches for incremental learning. Shahparast et al in [19] proposed two fast methods for adapting certainty factors of fuzzy rules, based on the reinforcement learning and reward and punishment.…”

Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

“…SNN have already proved that they are superior in learning and capturing spatiotemporal patterns from SSTD [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] (see also: http://ncs.ethz.ch/projects/evospike). SNN use temporal encoding of data as an internal mechanism to learn temporal relationships between input variables related to a spatio-temporal pattern that needs to be learned, classified and predicted.…”