Results 101 to 110 of about 561 (123)

Fault diagnosis for manipulators based on NeuCube

2015 11th International Conference on Natural Computation (ICNC), 2015
In this paper, a new fault diagnosis approach for manipulators based on spiking neural network is investigated. The newly proposed evolving spiking model is named NeuCube, it can be employed for classification, pattern recognition, and other kinds of problems.
null Shiying Pan, null Xiuqing Wang, null Peng Zhang   +2 more
openaire   +2 more sources

Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2017
This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1].
Nikola K. Kasabov, Maryam Gholami Doborjeh, Zohreh Gholami Doborjeh   +2 more
openaire   +3 more sources

NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data

Neural Networks, 2014
The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular ...
N. Kasabov
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Modelling Absence Epilepsy seizure data in the NeuCube evolving spiking neural network architecture

2015 International Joint Conference on Neural Networks (IJCNN), 2015
Epilepsy is the most diffuse brain disorder that can affect people's lives even on its early stage. In this paper, we used for the first time the spiking neural networks (SNN) framework called NeuCube for the analysis of electroencephalography (EEG) data recorded from a person affected by Absence Epileptic (AE), using permutation entropy (PE) features.
Elisa Capecci, Josafath I. Espinosa-Ramos, Nadia Mammone, Nikola Kasabov, Jonas Duun-Henriksen, Troels Wesenberg Kjaer, Maurizio Campolo, Fabio La Foresta, Francesco C. Morabito   +8 more
openaire   +2 more sources

Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube

Springer Series on Bio- and Neurosystems, 2018
This chapter introduces brain-inspired evolving SNN (BI-SNN) in which both the SNN architecture and learning are inspired by the structure, organisation and learning in the human brain. BI-SNN manifest deep learning from data and deep knowledge representation inspired by human brain as discussed in Chap. 3 of the book.
N. Kasabov
openaire   +2 more sources

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

International Conference on Neural Information Processing, 2019
In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate.
Dhvani Shah, Grace Y. Wang, Maryam Doborjeh, Zohreh Doborjeh, Nikola Kasabov   +4 more
openaire   +2 more sources

NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation

International Conference on Neural Information Processing, 2013
Classification and knowledge extraction from complex spatio-temporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of ...
Scott, N, Kasabov, N, Indiveri, G
openaire   +3 more sources

FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network Architecture and Finite Automata Theory

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving ...
Kaushalya Kumarasinghe, Mahonri Owen, Denise Taylor, Nikola Kasabov, Chi Kit   +4 more
openaire   +2 more sources

Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture

International Conference on Neural Information Processing, 2014
This paper presents a new method and a case study on fMRI spatio- and spectro-temporal data (SSTD) classification with the use of the recently proposed NeuCube architecture [1]. NeuCube is a three dimensional brain-like model of evolving spiking neurons that can be trained with SSTD such as fMRI, EEG and other brain data. This SSTD is mapped, analyzed,
Norhanifah Murli, Nikola Kasabov, Bana Handaga   +2 more
openaire   +2 more sources

Analysis of Gene Expression Time Series Data of Ebola Vaccine response using the NeuCube and Temporal Feature Selection

2018 International Joint Conference on Neural Networks (IJCNN), 2018
The purpose of this paper was to investigate a pipeline for processing temporal gene expression data using spiking neural networks and temporal feature selection techniques that would allow for genomic marker discovery. A promising temporal feature selection method was tested using the NeuCube for classification against a set of previously identified ...
Lucien Koefoed, Elisa Capecci, Nikola Kasabov   +2 more
openaire   +2 more sources