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.
Diaper, Dan and Huyck, Christian R. (2021) Cell Assembly-based Task Analysis (CAbTA). In: Computing Conference 2021 (formerly called Science and Information (SAI) Conference), 15-16 July 2021, Virtual (from London, UK).
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
Wilkinson, Kate and Dafoulas, George and Garelick, Hemda and Huyck, Christian R. (2020) 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 , 51 (3). pp. 761-777. ISSN 0007-1013
Huyck, Christian R. (2020) A neural cognitive architecture. Cognitive Systems Research , 59 . pp. 171-178. ISSN 1389-0417