Results 91 to 100 of about 251 (118)

Sleep Stage Classification using NeuCube on SpiNNaker: a Preliminary Study

2020 International Joint Conference on Neural Networks (IJCNN), 2020
This paper studies sleep stage classification using NeuCube, a Spiking Neural Network (SNN) architecture, simulated on SpiNNaker, a neuromorphic computer. The sleep electroencephalogram (EEG) time series is converted to spikes and provided as an input to NeuCube. Relevant feature vectors are extracted at different stages of training.
Sugam Budhraja, Basabdatta Sen Bhattacharya, Zohreh Doborjeh   +2 more
exaly   +4 more sources

An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

International Journal of Cloud Applications and Computing, 2018
This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system.
Ammar Almomani, Mohammad Alauthman, Firas AlBalas, Osama M. Dorgham, Atef A. Obeidat   +4 more
openaire   +2 more sources

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

, 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 ...
Nathan Matthew Scott, Nikola K. Kasabov, Giacomo Indiveri   +2 more
openaire   +3 more sources

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Feasibility of NeuCube spiking neural network architecture for EMG pattern recognition

2015 International Conference on Advanced Mechatronic Systems (ICAMechS), 2015
Multichannel electromyography (EMG) signals have been used as human-machine interface (HMI) for the control of pattern-recognition based prosthetic system in recent years. This paper is a feasibility analysis of using recently proposed NeuCube spiking neural network (SNN) architecture for a 6-class recognition problem of hand motions.
Zeng-Guang Hou, Nikola Kasabov, Gui-Bin Bian   +2 more
exaly   +2 more sources

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

, 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 Gholami Doborjeh, Nikola K. Kasabov   +4 more
openaire   +2 more sources

NeuCube^(ST) for spatio-temporal data predictive modelling with a case study on ecological data [PDF]

2014 International Joint Conference on Neural Networks (IJCNN), 2014
Limited by visual percepts elicited by existing visual prosthesis, it’s necessary to enhance its functionality to fulfill some challenging tasks for the blind such as obstacle avoidance.
Nikola Kasabov, Muhaini Othman, Yuxiao Li   +2 more
exaly   +3 more sources

Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCIapplications

2014 International Joint Conference on Neural Networks (IJCNN), 2014
The paper is a feasibility analysis of using the recently introduced by one of the authors spiking neural networks architecture NeuCube for modelling and recognition of complex EEG spatio-temporal data related to both physical and intentional (imagined) movements.
Denise Taylor, Nikola Kasabov, Elisa Capecci   +2 more
exaly   +2 more sources

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

Lecture Notes in Computer Science, 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,
Nikola Kasabov, Bana Handaga, Kasabov Nikola   +2 more
exaly   +2 more sources

From von Neumann Architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications

, 2018
Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the “hidden” interactions and interrelationship among multivariate data, is vital in SSTD investigation.
Doborjeh, Maryam, Doborjeh, Zohreh, Gollahalli, Akshay Raj, Kumarasinghe, Kaushalya, Breen, Vivienne, Sengupta, Neelava, Ramos, Josafath Israel Espinosa, Hartono, Reggio, Capecci, Elisa, Kawano, Hideaki, Othman, Muhaini, Lei, Zhou, Jie, Yang, Bose, Pritam, Chenjie, Ge   +14 more
openaire   +3 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 Kasabov, Zohreh Doborjeh
exaly   +3 more sources