Framework for Knowledge Driven Optimisation Based Data Encoding for Brain Data Modelling Using Spiking Neural Network Architecture

Sengupta, Neelava; Scott, Nathan Matthew; Kasabov, Nikola

doi:10.1007/978-3-319-27212-2_9

Cited by 12 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sengupta, Scott, and Kasabov interpret the encoding process as a data compression problem with background knowledge [83] and introduce the GaGamma scheme. Thereby, information has to be maximised while minimising the spike density.…”

Section: Filter and Optimizer-based Approachesmentioning

confidence: 99%

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Auge

Hille

Mueller

et al. 2021

Neural Process Lett

129

View full text Add to dashboard Cite

Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

show abstract

Section: Filter and Optimizer-based Approachesmentioning

confidence: 99%

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Auge

Hille

Mueller

et al. 2021

Neural Process Lett

129

View full text Add to dashboard Cite

show abstract

“…The dynamic evolving Spike Neural Networks (deSNN) is used here as an output classifier. Our choice of the NeuCube platform was influenced by its outstanding success that it has achieved on spatio-temporal classification problems in many diverse (Kasabov et al 2016;Dhoble et al 2012;Kasabov et al 2013;Kasabov 2014;Sengupta et al 2015;Kasabov 2007). All such studies employing NeuCube have been on data that exhibit a relatively fast pace of temporal changes.…”

Section: Introductionmentioning

confidence: 99%

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks

2017

Self Cite

View full text Add to dashboard Cite

This research study proposes a novel method of interrelated problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition. The well-known FG-NET and MORPH Album 2 image gallery were used and anthropometric features were extracted from landmark points on the face. The landmarks were pre-processed with the procrustes algorithm before feature extraction was performed. The Weka machine learning workbench was used to compare the performance of traditional techniques such as the K nearest neighbor (Knn) and Multi-LayerPerceptron (MLP) with NeuCube. Our empirical results show that Neu-Cube performed consistently better across both problem types that we investigated.

show abstract

From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and Implementations

Sengupta

Espinosa-Ramos

Tu³

et al. 2018

Studies in Computational Intelligence

View full text Add to dashboard Cite

Framework for Knowledge Driven Optimisation Based Data Encoding for Brain Data Modelling Using Spiking Neural Network Architecture

Cited by 12 publications

References 19 publications

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks

From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and Implementations

Contact Info

Product

Resources

About