Neural networks: An overview of early research, current frameworks and new challenges
By A. Prieto, B. Prieto, E. M. Ortigosa, E. Ros, F. Pelayo, J. Ortega, Ignacio Rojas. Neurocomputing, 2016
321 citations recorded by the source corpus.
This paper presents a comprehensive overview of modelling, simulation and implementation of neural networks, taking into account that two aims have emerged in this area: the improvement of our understanding of the behaviour of the nervous system and the need to find inspiration from it to build systems with the advantages provided by nature to perform certain relevant tasks. The development and evolution of different topics related to neural networks is described (simulators, implementations, and real-world applications) showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems. The paper also shows how, over time, artificial neural networks have contributed to fundamental concepts at the birth and development of other disciplines such as Computational Neuroscience, Neuro-engineering, Computational Intelligence and Machine Learning. A better understanding of the human brain is considered one of the challenges of this century, and to achieve it, as this paper goes on to describe, several important national and multinational projects and initiatives are marking the way to follow in neural-network research.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ef065fa6804edce3919fd3cbeed35cf56e3d094f
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Citations in the corpus
- Global optimization issues in deep network regression: an overview (2018, background): “the recent surveys [114]) and also on more classical nonlinear parametric or semi-parametric regression methods [11,74,118,119].”
- Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “However RNN modelss suffer from the exploding or vanishing gradient which makes it difficult to train (Prieto et al. 2016).”
- Machine learning algorithms to predict flow condensation heat transfer coefficient in mini/micro-channel utilizing universal data (2020, background): “Generally, these models are considered as black oxes that are unable to provide any insight about the system feaures and also have unknown parameters known as “weights” [86] .”
- Power transmission and distribution losses – A model based on available empirical data and future trends for all countries globally (2019, methodology): “These 114 values are then summed up with bias and then presented as a result [18].”