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Neurocomputing

Resumen/Descripción – provisto por la editorial en inglés
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

NEW! Neurocomputing's Software Track allows you to expose your complete Software work to the community through a novel Publication format: the Original Software Publication

Overview:

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.

Types of publications:

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neurocomputing Letters allow for the rapid publication of special short communications.

NEW! The Neurocomputing Software Track

Neurocomputing Software Track publishes a new format, the Original Software Publication (OSP) to disseminate exiting and useful software in the areas 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. We encourage high-quality original software submissions which contain non-trivial contributions in the above areas related to the implementations of algorithms, toolboxes, and real systems. The software must adhere to a recognized legal license, such as OSI approved licenses.

Importantly, the software will be a full peer reviewed publication that is able to capture your software updates once they are released. To fully acknowledge the author's/developers work your software will be fully citable as an Original Software Publication, archived and indexed and available as a complete online "body of work" for other researchers and practitioners to discover.

See the detailed Submission instructions, and more information about the process for academically publishing your Software: here
Palabras clave – provistas por la editorial

No disponibles.

Disponibilidad
Institución detectada Período Navegá Descargá Solicitá
No detectada desde ene. 1989 / hasta dic. 2023 ScienceDirect

Información

Tipo de recurso:

revistas

ISSN impreso

0925-2312

ISSN electrónico

1872-8286

Editor responsable

Elsevier

País de edición

Países Bajos

Fecha de publicación

Tabla de contenidos

Apprenticeship learning with few examples

Abdeslam Boularias; Brahim Chaib-draa

Palabras clave: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications.

Pp. 83-96

Sentiment recognition of online course reviews using multi-swarm optimization-based selected features

Zhi Liu; Sanya Liu; Lin Liu; Jianwen Sun; Xian Peng; Tai Wang

Palabras clave: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications.

Pp. 11-20

An improved reinforcement learning algorithm based on knowledge transfer and applications in autonomous vehicles

Derui DingORCID; Zifan Ding; Guoliang Wei; Fei HanORCID

Palabras clave: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications.

Pp. 243-255

Qauxi: Cooperative multi-agent reinforcement learning with knowledge transferred from auxiliary task

Wenqian Liang; Ji Wang; Weidong Bao; Xiaomin Zhu; Guanlin Wu; Dayu Zhang; Liyuan Niu

Palabras clave: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications.

Pp. 163-173

Transformer for Skeleton-based Action Recognition: A Review of Recent Advances

Wentian Xin; Ruyi Liu; Yi Liu; Yu Chen; Wenxin Yu; Qiguang Miao

Palabras clave: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications.

Pp. No disponible