Neurocomputing

About the journal

Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered. Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition. Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices). Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Articles

• David Simões, Nuno Lau, Luis Paulo Reis. Multi-agent actor centralized-critic with communication. Neurocomputing, January 2020
• Hamidreza Kasaei, Miguel Riem Oliveira, Gi Hyun Lim, Luís Seabra Lopes, Ana Maria Tomé. Towards Lifelong Assistive Robotics: A Tight Coupling Between Object Perception and Manipulation. Neurocomputing, vol. 291, p. 151-166, May 2018
• Lachezar Bozhkov, Petia Koprinkova-Hristova, Petia Georgieva. Reservoir Computing for Emotion Valence Discrimination from EEG signals. Neurocomputing, vol. 231, p. 28-40, March 2017
• R. Schachtner, G. Pöppel, Ana Maria Tomé, C.G. Puntonet, Elmar W. Lang. A new Bayesian approach to nonnegative matrix factorization: uniqueness and model order selection. Neurocomputing, vol. 138, p. 142-156, August 2014
• Mohammed S. Al-Rawi, João Paulo Cunha. On using permutation tests to estimate the classification significance of functional magnetic resonance imaging data. Neurocomputing, vol. 82, p. 224–233, April 2012
• Ana Rita Teixeira, Ana Maria Tomé, Elmar W. Lang. Unsupervised feature extraction via kernel subspace techniques. Neurocomputing, vol. 74, no. 5, p. 820-830, February 2011
• K. Stadlthanner, F. J. Theis, Elmar W. Lang, Ana Maria Tomé, C. G. Puntonet, J. M. Gorriz. Hybridizing sparse component analysis with genetic algorithms for microarray analysis. Neurocomputing, vol. 71, no. 10-12, p. 2356–2376, June 2008
• K. Stadlthanner, Ana Maria Tomé, F. J. Theis, Elmar W. Lang, W. Gronwald, H. R. Kalbitzer. Separation of water artifacts in 2D NOESY protein spectra using congruent matrix pencils. Neurocomputing, vol. 69, no. 4-6, p. 497–522, January 2006
• P. Gruber, K. Stadthanner, M. Boehm, F. J. Theis, Elmar W. Lang, Ana Maria Tomé, Ana Rita Teixeira, C. G. Puntonet, J. M. Gorriz Saez. Denoising using local projective subspace methods. Neurocomputing, vol. 69, no. 13-15, p. 1485–1501, August 2006
• Mohammed S. Al-Rawi. A neural network to solve the hybrid N-parity: Learning with generalization issues. Neurocomputing, vol. 68, p. 273-280, October 2005

Impact factor

The impact factor of Neurocomputing is as follows: