Neural Engineering Object

Some form of Nengo has existed since 2003. 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 modelling 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 of a back-end agnostic API. This most recent iteration of Nengo defines a specific Python-based scripting API with back-ends targeting Numpy, OpenCL and Neuromorphic hardware such as Spinnaker.{{cite journal|last1=Friedl|first1=K. E.|last2=Voelker|first2=A. R.|last3=Peer|first3=A.|last4=Eliasmith|first4=C.|title=Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch|journal=IEEE Robotics and Automation Letters|date=1 January 2016|volume=1|issue=1|pages=516–523|doi=10.1109/LRA.2016.2517213|s2cid=6401430 |issn=2377-3766|url=http://eprints.uwe.ac.uk/31681/1/submission_final.pdf}}[https://www.nengo.ai/nengo/history.html Nengo History]; retrieved 2016-10-28. This newest iteration also comes with an interactive GUI to help with the quick prototyping of neural models.[https://github.com/nengo/nengo_gui Nengo GUI source code]; retrieved 2016-10-28.

Nengo uses a custom license which allows free personal and research use, but a paid license is required for commercial purposes.[https://github.com/nengo/nengo/blob/master/LICENSE.rst Nengo License]; retrieved 2016-10-28.

Nengo is built upon two theoretic underpinnings, the Neural Engineering Framework (NEF){{cite book|last1=Eliasmith|first1=Chris|last2=Anderson|first2=Charles H.|title=Neural engineering : computation, representation, and dynamics in neurobiological systems|date=2003|publisher=MIT Press |isbn=9780262550604}} and the Semantic Pointer Architecture (SPA).{{harvnb|Eliasmith|2013}}

Nengo differs primarily from other modelling software in the way it models connections between neurons and their strengths. Using the NEF,{{cite tech report |first=Terrence C. |last=Stewart |title=A technical overview of the neural engineering framework |date=2012 |volume=110 |publisher=Centre for Theoretical Neuroscience, University of Waterloo |url=http://compneuro.uwaterloo.ca/files/publications/stewart.2012d.pdf}} Nengo allows defining connection weights between populations of spiking neurons by specifying the function to be computed, instead of forcing the weights to be set manually, or use a learning rule to configure them from a random start.[http://nengo.ca/faq Nengo FAQ]; retrieved 2016-10-28. That being said, these aforementioned traditional modelling methods are still available in Nengo.

To represent symbols in Nengo, SPA is used. Many aspects of human cognition are easier to model using symbols. In Nengo, these are presented as vectors with a set of operations associated to them. These vectors and their operations are called SPA. SPA has been used to model human linguistic search{{cite book |first1=Ivana |last1=Kajić |first2=Jan |last2=Gosmann |first3=Terrence C. |last3=Stewart |first4=Thomas |last4=Wennekers |first5=Chris |last5=Eliasmith |chapter=Towards a cognitively realistic representation of word associations |chapter-url=https://cogsci.mindmodeling.org/2016/papers/0379/paper0379.pdf |title=Proceedings of the 38th Annual Conference of the Cognitive Science Society |publisher=Cognitive Science Society |location= |date=2016 |isbn=978-0-9911967-3-9 |pages=2183–8 |url=https://cogsci.mindmodeling.org/2016/index.html}} and task planning.{{cite book |first1=Peter |last1=Blouw |first2=Chris |last2=Eliasmith |first3=Brian |last3=Tripp |chapter=A scaleable spiking neural model of action planning |chapter-url=https://cogsci.mindmodeling.org/2016/papers/0279/index.html |title=Proceedings of the 38th Annual Conference of the Cognitive Science Society |publisher=Cognitive Science Society |date=2016 |isbn=978-0-9911967-3-9 |pages=1583–8 |url=https://cogsci.mindmodeling.org/2016/index.html}}

Notable developments accomplished using the Nengo software have occurred in many fields, and Nengo has been used and cited in over 100 publications.{{Cite web |url=https://www.nengo.ai/publications.html |title=Publications — Nengo 2017-11-23 documentation |access-date=2018-02-02 |archive-url=https://web.archive.org/web/20180203010937/https://www.nengo.ai/publications.html |archive-date=2018-02-03 |url-status=dead }} An important development to note is Spaun, a network of 6.6 million{{cite thesis |author=Xuan Choo |title=Spaun 2.0: Extending the World's Largest Functional Brain Model |type=PhD thesis |publisher=University of Waterloo |date=2018 |hdl=10012/13308}} artificial spiking neurons (a small number compared to the number in the human brain), which uses groups of these neurons to complete cognitive tasks via flexible coordination. Spaun is the world's largest functional brain model, and can be used to test hypotheses in neuroscience.{{cite journal |last1=Eliasmith |first1=C. |last2=Stewart |first2=T.C. |last3=Choo |first3=X. |last4=Bekolay |first4=T. |last5=DeWolf |first5=T. |last6=Tang |first6=Y. |last7=Rasmussen |first7=D. |title=A large-scale model of the functioning brain |journal=Science |volume=338 |issue=6111 |pages=1202–5 |date=2012 |doi=10.1126/science.1225266 |pmid=23197532|bibcode=2012Sci...338.1202E |s2cid=1673514 }}

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• {{cite book |isbn= 978-0199794546 |title=How To Build A Brain |first=Chris |last=Eliasmith |publisher=Oxford University Press|year=2013}}

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Category:Neural network software