Results 11 to 20 of about 561 (123)

Spiking Neural Networks: Background, Recent Development and the NeuCube Architecture. [PDF]

Neural Processing Letters, 2020
This paper reviews recent developments in the still-off-the-mainstream information and data processing area of spiking neural networks (SNN)—the third generation of artificial neural networks.
A Tavanaei, AL Hodgkin, AV Herz, B Petro, C Morris, C Oreilly, C Wilson, Clarence Tan, D Drubach, D Silver, DH Hubel, E Trentin, ED Adrian, EM Izhikevich, EM Izhikevich, F Rosenblatt, G Buzsaki, G-Q Bi, J Gautrais, JL Hindmarsh, JR Huguenard, K Fukushima, L Lapicque, L Paulun, LF Abbott, M Soleymani, Marko Šarlija, N Ganapathy, N Kasabov, N Kasabov, N Kasabov, N Kasabov, N Kasabov, N Sengupta, N Sengupta, Nikola Kasabov, NK Kasabov, NK Kasabov, NK Kasabov, NK Kasabov, O Russakovsky, P O’Connor, PA Merolla, PU Diehl, R Brette, R FitzHugh, R Lestienne, S Cassenaer, S Koelstra, S Song, SG Wysoski, SM Bohte, SR Kheradpisheh, V Jacob, W Gerstner, W Gerstner, W Gerstner, W Maass, Y Cao, Y Mu, ZG Doborjeh   +60 more
core   +6 more sources

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications [PDF]

Neural Networks, 2015
The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM).
Alvi, F, Capecci, E, Doborjeh, M, Espinosa-Ramo, JI, Feigin, V, Gulyaev, S, Hartono, R, Hou, Z-G, Kasabov, N, Mahmoud, M, Marks, S, Murli, N, Othman, M, Scott, N, Sengupta, S, Taylor, D, Tu, E, Wang, G, Yang, J, Zhou, L   +19 more
core   +6 more sources

Analyzing darknet traffic through machine learning and neucube spiking neural networks

Intelligent and Converged Networks
The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels. Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.
Iman Akour, Mohammad Alauthman, Khalid M. O. Nahar, Ammar Almomani, Brij B. Gupta   +4 more
doaj   +3 more sources

Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling, and understanding of stream data. [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2016
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture.
Kasabov, Nikola, Tu, Enmei, Yang, Jie
core   +9 more sources

Longitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with aNeuCube Spiking Neural Network Model [PDF]

2016 International Joint Conference on Neural Networks (IJCNN), 2016
Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroencephalography (EEG) data collected from people affected by Alzheimer’s Disease (AD) and people diagnosed with mild cognitive impairment (MCI).
Capecci, E., Foresta, F, Gholami Doborjeh, Z, Kasabov, N, Mammone, N, Morabito, F   +5 more
core   +3 more sources

Personalised Modelling on Integrated Clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network System [PDF]

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data.
Gholami, M, Kasabov, N
core   +4 more sources

Classification and Segmentation of fMRI Spatio-temporal Brain Data With a Neucube Evolving Spiking Neural Network Model [PDF]

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014
The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured
Capecci, E, Doborjeh, MG, Kasabov, N
core   +4 more sources

From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications [PDF]

, 2018
Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time.
A Gollahalli, E Courchesne, Gregory O'Grady, H Baillieux, Hideaki Kawano, J-H Gao, M Kate, MG Doborjeh, N Kasabov, N Kasabov, N Kasabov, P Bose, Peng Du, RS Gill, V Fonov, Vittorio Gallese, VL Feigin, VS Fonov, Y Cevik   +18 more
core   +4 more sources

Using EEG Data and NeuCube for the Study of Transfer of Learning [PDF]

2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020
Deeper and long-lasting learning occurs through a critical review of prior knowledge in the light of the new context, and a transfer of the acquired knowledge to new settings. Attention to the task is one of factors that enable transfer of learning (TL). This study adopts a cognitive neuroscience approach to the study of TL.
Doborjeh, Maryam, Fard, Mojgan, Kasabov, Nikola, Petrova, Krassie   +3 more
core   +4 more sources

Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning

Neurocomputing, 2023
Electroencephalogram (EEG) data are produced in quantity for measuring brain activity in response to external stimuli. With the rapid development of brain-inspired intelligence, spiking neural network (SNN) possesses the potential to handle EEG data by using spiking activity transmitted among spatially located synapses and neurons.
Xuanyu Wu, Yixiong Feng, Shanhe Lou, Hao Zheng, Bingtao Hu, Zhaoxi Hong, Jianrong Tan   +6 more
openaire   +4 more sources