A 3D ray traced biological neural network learning model. [PDF]
Nat CommunYuen B, Dong X, Lu T.
europepmc +1 more source
Artificial intelligence for brain disease diagnosis using electroencephalogram signals. [PDF]
J Zhejiang Univ Sci BShang S +6 more
europepmc +1 more source
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. We provide background information about the functioning of biological neurons, discussing the most important and commonly used mathematical neural models. Most
Marko Sarlija +2 more
exaly +4 more sources
Related searches:
Improving NeuCube spiking neural network for EEG-based pattern recognition using transfer learning
Neurocomputing, 2023Electroencephalogram (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.
Shanhe Lou, Yixiong Feng, Bingtao Hu
exaly +4 more sources
Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 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. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a ...
Nikola Kasabov
exaly +5 more sources
Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications [PDF]
Neural Networks, 2016 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). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major ...
Josafath Israel Espinosa Ramos +2 more
exaly +4 more sources
Sleep Stage Classification using NeuCube on SpiNNaker: a Preliminary Study
2020 International Joint Conference on Neural Networks (IJCNN), 2020This 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.
Basabdatta Sen Bhattacharya +2 more
exaly +3 more sources
Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks
Studies in Computational Intelligence, 2018This paper presents a new approach for spatio-temporal road traffic forecasting that relies on the adoption of the NeuCube architecture based on spiking neural networks. The NeuCube platform was originally conceived and designed to process electroencephalographic (EEG) signals considering their temporal component and their spatial source within the ...
Elisa Capecci +2 more
exaly +2 more sources
Feasibility of NeuCube spiking neural network architecture for EMG pattern recognition
2015 International Conference on Advanced Mechatronic Systems (ICAMechS), 2015Multichannel 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 +2 more
exaly +2 more sources