- From: Dave Raggett <dsr@w3.org>
- Date: Sat, 15 Feb 2020 10:59:17 +0000
- To: paoladimaio10@googlemail.com
- Cc: Owen Ambur <Owen.Ambur@verizon.net>, W3C AIKR CG <public-aikr@w3.org>
- Message-Id: <4911EB5C-754D-4064-957F-B5FDC81C8D69@w3.org>
Hi Paola, > On 15 Feb 2020, at 01:17, Paola Di Maio <paola.dimaio@gmail.com> wrote: > > I am working on a Neurosymbolic approaches for AI KR. I hope that your paper will cite Chris Eliasmith’s computational neuroscience team at the University of Waterloo. They have worked extensively on biologically accurate models describing how symbolic information can be expressed and manipulated by neural networks. https://uwaterloo.ca/systems-design-engineering/profile/celiasmi <https://uwaterloo.ca/systems-design-engineering/profile/celiasmi> "Higher-level cognitive functions in biological systems are made possible by Semantic Pointers. Semantic Pointers are neural representations that carry partial semantic content and are composable into the representational structures necessary to support complex cognition." Semantic pointers are vectors in n-dimensional spaces corresponding to concurrent patterns of firing across bundles of nerve fibres. It is essentially the same idea as a chunk, i.e. a collection of properties, whose values identify other chunks. Using addition and circular convolution, multiple semantic pointers can be stored and retrieved within a single vector. Mathematical details are available at: https://www.nengo.ai/nengo-spa/user-guide/spa-intro.html <https://www.nengo.ai/nengo-spa/user-guide/spa-intro.html> There is also a downloadable python package for testing and deploying neural networks, see: https://www.nengo.ai/ <https://www.nengo.ai/> Best regards, Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett W3C Data Activity Lead & W3C champion for the Web of things
Received on Saturday, 15 February 2020 10:59:22 UTC