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Neural Engineering Object (NENGO) is a graphical and scripting software for simulating large-scale neural systems.〔Stewart, Terrence C. ''et al.'' ( "Python Scripting in the Nengo Simulator," ) ''Frontiers in Neuroinformatics.'' 2009; 3: 7; retrieved 2013-8-23.〕 As Neural network software NENGO is a powerful tool for modeling neural networks with useful applications towards solving problems in cognitive science, psychology, neuroscience. Note that NENGO is the opposite, dual or inverse of using neural models to solve non-neural problems in computing. For example, artificial ants, neural networks, swarm intelligence and other bio-inspired AI is being applied to numerous fields, from natural language and disability to manufacturing, combinatorial optimization, discrete math, robotics, classification, machine learning, and many others. NENGO is using computing to model bio/ neurons in simulated brains; Bio-inspired is modeling computing solutions after real organisms, including human brains and other superorganisms, colonies, hives, etc. The distinction is nuanced, there are overlaps, and one can also be seen as the applied science of the other.〔Introduction to Artificial Ants, Monmarche, 2010, Wiley, 978-1848211940〕 == Background and implementation == Some form of Nengo has existed since 2003. The development process has brought Nengo to a fourth generation as modeling software. Originally developed as a Matlab script under the name NESim (Neural Engineering Simulator), it was later moved to a Java implementation under the name NEO, and then eventually Nengo. The first three generations of Nengo developed with a focus on developing a powerful modeling tool with a simple interface, and scripting system. As the tool became increasingly useful the limitations of the system in terms of speed led to development using backends that differed from the original Jython backend. An important implementation favored for its processing speed and power is the Theano computational library backend. The current generation of development is centered around the work on Nengo API, with the purpose of creating a single front end to the multiple viable backend implementations.〔(Nengo API 0.1 documentation ); retrieved 2013-8-23.〕 Nengo is developed by several labs at the Centre for Theoretical Neuroscience (CTN) at the University of Waterloo in Ontario, Canada.〔( "Contact" at nengo.ca ); retrieved 2013-8-23.〕 As open source software Nengo is licensed under the Mozilla Public License 1.1 (MPL 1.1),〔(Nengo License GITHUB )〕 allowing for work and development, as well as forking, by many independent developers. Working under the Nengo API this forking should allow for multiple implementations of Nengo by many programmers to be used with the vast majority of developed models. Nengo differs primarily from other modeling software in the way it models connections between neurons and their strengths. Nengo allows for a level of abstraction that provides ease of use by allowing for the specification of connection weights using overall functions to be computed, instead of forcing you to set the weights manually, or use a learning rule to configure them from a random start.〔(Nengo FAQ )〕 Traditional Modeling techniques are still available in the software package, and though programming languageNengo allows for higher level abstraction, the options to manually set weights, or to use a variety of learning rules still exist. The major provider of Nengo's higher level functionality is the framework on which this function level modeling is built. This is the Neural Engineering Framework (NEF), a general model that allows the construction of large-scale plausible neural models using realistic spiking neurons to implement arbitrary algorithms.〔Terrence C. Stewart. A technical overview of the neural engineering framework. Technical Report, Centre for Theoretical Neuroscience, 2012.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Neural Engineering Object」の詳細全文を読む スポンサード リンク
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