期刊缩写       NEURAL NETWORKS

期刊全称       NEURAL NETWORKS 神经网络

期刊ISSN      0893-6080

2013-2014最新影响因子     2.076

期刊官方网站       http://www.elsevier.com/wps/find/journaldescription.cws_home/841/description

              http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html

期刊投稿网址       http://ees.elsevier.com/neunet/

通讯方式       PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND, OX5 1GB

涉及的研究方向    工程技术-计算机：人工智能

出版国家       ENGLAND

出版周期       Monthly

出版年份       1988

年文章数       160

 

 

Neural Networks

 

A neural network is an interconnected group of biological neurons. In modern usage the term can also refer to artificial neural networks, which are constituted of artificial neurons. Thus the term 'Neural Network' specifies two distinct concepts:

 

- A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or the central nervous system.

 

- In the field of neuroscience, it most often refers to a group of neurons from a nervous system that are suited for laboratory analysis.

 

Artificial neural networks were designed to model some properties of biological neural networks, though most of the applications are of technical nature as opposed to cognitive models. Neural networks are made of units that are often assumed to be simple in the sense that their state can be described by single numbers, their "activation" values. Each unit generates an output signal based on its activation. Units are connected to each other very specifically, each connection having an individual "weight" (again described by a single number). Each unit sends its output value to all other units to which they have an outgoing connection. Through these connections, the output of one unit can influence the activations of other units. The unit receiving the connections calculates its activation by taking a weighted sum of the input signals (i.e. it multiplies each input signal with the weight that corresponds to that connection and adds these products). The output is determined by the activation function based on this activation (e.g. the unit generates output or "fires" if the activation is above a threshold value). Networks learn by changing the weights of the connections. In general, a neural network is composed of a group or groups of physically connected or functionally associated neurons. A single neuron can be connected to many other neurons and the total number of neurons and connections in a network can be extremely large. Connections, called synapses are usually formed from axons to dendrites, though dendrodentritic microcircuits and other connections are possible. Apart from the electrical signalling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. Thus, like other biological networks, neural networks are extremely complex.

 

While a detailed description of neural systems seems currently unattainable, progress is made towards a better understanding of basic mechanisms. Artificial intelligence and cognitive modeling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots. Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimisation and control theory. The cognitive modelling field is the physical or mathematical modelling of the behaviour of neural systems; ranging from the individual neural level (e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism (e.g. behavioural modelling of the organism's response to stimuli).

 

 

The Official Journal of the International Neural Network Society, European Neural Network Society & Japanese Neural Network Society

 

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

 

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters and reviews, as well as letters to the editor, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.

 

The journal is published twelve times a year. Neural Networks can be accessed electronically via Science Direct (http://www.sciencedirect.com/science/journal/08936080), which is used by over eight million individuals world-wide.

 

Benefits to authors

We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.

 

Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

 

 

Co-Editors-in-Chief

Kenji Doya

Okinawa Inst. of Science & Tech., Onna, Okinawa, Japan

Email Kenji Doya

 

DeLiang Wang

Ohio State University, Columbus, Ohio, USA

Email DeLiang Wang

 

Founding Editor-in-Chief

 

Current Events Editor

Paul Pang

UNITEC Institute of Technology, Auckland, New Zealand

Email Paul Pang