My current research focus is in recurrent Neural Networks known as Cell Assemblies. This was implemented in the EPSRC funded CABot project to develop a Cell Assembly roBot that view the environment, takes natural language commands from the user, and maintains its own goal. Cell Assemblies are a computational model derived from mammalian neural, and psychological evidence. They are also a novel computational medium that generate active symbols. This mechanism gives a reasonable (though of course incomplete) explanation on how people think. Moreover, it is mechanism that could eventually be applied to a wide range of real world tasks. I have moved into this area as a way of implementing semantics for Natural Language Processing, but it could be used for a wide range of other problems. We are exploring the use of CAs for categorisation, speech recognition, cognitive models, and agent technology.
Huyck, Christian R. (2020) Learning categories with spiking nets and spike timing dependent plasticity. In: 40th SGAI 2020, 15-17 Dec 2020, Cambridge, UK.
Huyck, Christian R. and Vergani, Alberto Arturo (2020) Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. Journal of Computational Neuroscience , 48 (3). pp. 299-316. ISSN 0929-5313
Huyck, Christian R. (2020) A neural cognitive architecture. Cognitive Systems Research , 59 . pp. 171-178. ISSN 1389-0417
Wilkinson, Kate and Dafoulas, George and Huyck, Christian R. and Garelick, Hemda (2019) Are quiz-games an effective revision tool in Anatomical Sciences for Higher Education and what do students think of them? British Journal of Educational Technology . ISSN 0007-1013 (Published online first)
Ji, Yuehu and Gamez, David and Huyck, Christian R. (2018) A brain-inspired cognitive system that mimics the dynamics of human thought. In: AI-2018 Thirty-eighth SGAI International Conference on Artificial Intelligence, 11-13 Dec 2018, Cambridge, UK.