Results 51 to 60 of about 561 (123)

A Computational Model of Neuroreceptor-Dependent Plasticity (NRDP) Based on Spiking Neural Networks [PDF]

, 2019
Activity-dependent plasticity has attracted the interest of researchers for years in the domain of computational neuroscience, as the modification of synaptic efficacy occurs as result of complex biochemical mechanisms that take placeat a cellular level.
Capecci, Elisa, Espinosa-Ramos , Josafath, Kasabov, Nikola   +2 more
core   +1 more source

Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment [PDF]

, 2019
Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and ...
Afifi Abdullah, Mohd Hafizul, Aisyah Mohamed, Siti, Kasim, Shahreen, Othman, Muhaini   +3 more
core   +2 more sources

Review of medical data analysis based on spiking neural networks

, 2023
Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions.
Li, X., Liu, D., Wang, H., Wang, L., Yi, X., Zhang, B., Zhang, B., Zhang, X., Zhao, D.   +8 more
core  

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory [PDF]

, 2018
Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind.
Carlos Valle, Erick López, Esteban Gil, Henrik Madsen, Héctor Allende   +4 more
core   +2 more sources

Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data

, 2023
<p>EEG data collected from several subjects when perfoming complex spatio-temporal tasks </p>
Jie Yang, Enmei Tu, Maryam Doborjeh, Yongyao Tan Tan, Nikola Kasabov   +4 more
openaire   +1 more source

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? [PDF]

Brain Sci, 2023
Pham MD, D'Angiulli A, Dehnavi MM, Chhabra R.   +3 more
europepmc   +1 more source

Diagnostic biomarker discovery from brain EEG data with LSTM, reservoir-SNN and NeuCube: Methods and a pilot study on epilepsy vs migraine

, 2023
<p>The paper explores how deep LSTM and deep spiking neural networks (SNN) can be used to extract meaningful features from spatio-temporal EEG brain data for early, on-line diagnosis. It introduces a new online spike encoding algorithm for Izhikevich neural networks and new methods for learning and diagnostic biomarker discovery for each of the ...
openaire   +1 more source

A study on liquid state machine for pattern recognition - [PDF]

, 2016
Thesis. M.E. American University of Beirut. Department of Electrical and Computer Engineering, 2016. ET:6344Advisor : Dr. Mariette Awad, Associate Professor, Electrical and Computer Engineering ; Committee Members : Dr. Mohamad Adnan Al-Alaoui, Professor,
Al Zoubi, Obada Mohammad Yasser,
core  

Neuromorphic computing facilitates deep brain-machine fusion for high-performance neuroprosthesis. [PDF]

Front Neurosci, 2023
Qi Y, Chen J, Wang Y.
europepmc   +1 more source

EEG-Based Depression Classification and Brain Region Analysis Using a Hybrid of NeuCube and Dictionary Learning Framework

Abstract The Study of depression and its effects on the brain is essential since this common mental health disorder affects millions. In addition to disturbing emotional and cognitive processes, depression also disrupts activity in discrete brain regions. Identifying these distortions is important for expanding the diagnosis and treatment plans.
F Alenizi, B Bouallegue, Ali Sam, R Boostani   +3 more
openaire   +1 more source