Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TLDR
The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.About:
This article is published in Neurocomputing.The article was published on 2016-11-19. It has received 184 citations till now. The article focuses on the topics: Neural modeling fields & Nervous system network models.read more
Citations
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Journal ArticleDOI
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TL;DR: The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
Journal ArticleDOI
Recommendation system based on deep learning methods: a systematic review and new directions
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
Journal ArticleDOI
Deep learning approach for microarray cancer data classification
TL;DR: A deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes using a 7-layer deep neural network architecture having various parameters for each dataset is developed.
Journal ArticleDOI
Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
TL;DR: A thorough review on the development of ML-ELMs, including stacked ELM autoencoder, residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications, and the connection between random neural networks and conventional deep learning.
References
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Journal ArticleDOI
Multi-level network modeling of cortical dynamics built on the GENESIS environment
TL;DR: Development of standardized model description languages would facilitate future integrative efforts, allowing easy combination of desired models and tools from different simulators in one modeling study.
The SpiNNaker Project This paper describes the design of a massively parallel computer that is suitable for computational neuroscience modeling of large-scale spiking neural networks in biological real time.
TL;DR: The current state of the spiking neural network architecture project is reviewed, and the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world is presented.
Proceedings ArticleDOI
Optodigital implementation of a neural network using a joint transform correlator based in a Hopfield inner product model for character recognition
TL;DR: The design and implementation of a scheme which takes the advantages of both systems to develop an hybrid opto-digital processor, with applications in character recognition is presented, based in the Hopfield inner products model using a Joint Transform Correlator.
Journal ArticleDOI
Nongeometric and nonparametric pattern recognitions using smarter neurons for size-limited nets
TL;DR: Only if a new technology is unquestionably successful and clearly has been demonstrated to be an order of magnitude better than evolving traditional methods will new technologies such as neural networks be adopted.