A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection

By Samanwoy Ghosh-Dastidar, Hojjat Adeli. Neural Networks, 2009

452 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/039012597bdd68591a0a71e156c7e2855714b244

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, background): “al.(36) Mixed-band feature space Multi-Spiking Neural Network 90.”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “Using spiking neurons, a new generation of neural networks has been developed, called Spiking Neural Networks (SNN) [40-42].”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, unknown): “The features extracted from the detailed coefficients at various levels or different frequency bands can reveal the characteristics of the time series and can be used in automated seizure detection systems [13,14].”
Enhanced probabilistic neural network with local decision circles: A robust classifier (2010, unknown): “…radial basis function neural networks [9, 33,44,45,78], neuro-fuzzy networks [5,6,34,39,71], wavelet neural networks [29,72,98], spiking neural networks [30–32, In this paper, an enhanced PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming.”
Deep Learning in Spiking Neural Networks (2018, methodology): “Later advanced versions of SpikeProp, MultiSpikeProp, were applicable in multiple spike coding [106], [107].”
Automated EEG analysis of epilepsy: A review (2013, background): “Ghosh-Dastidar and Adeli [36] Mixed-band feature space Multi-spiking neural network 90.”
Introduction to spiking neural networks: Information processing, learning and applications. (2011, methodology): “…of the self-organizing maps (Ruf and Schmitt 1998, Ruf 1998), temporal sequence recognition based on the subspaces clustering (Natschlaeger and Ruf 1998a), formation of associative memories (Knoblauch 2003), or extraction of principal components from spike trains (Gerstner and Kistler 2002b).”
Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals (2012, background): “GhoshDastida r et al.(30) Mixed-band feature space (9) MultiSpiking Neural Network 90.”