Daniel Rasmussen
PhD candidate
Former PhD candidate in Computer Science at the University of Waterloo, working with Chris Eliasmith in the Centre for Theoretical Neuroscience. You can find my full CV here.
Education
I completed both my Master's and PhD at the CTN, in 2010 and 2014 respectively. You can find my theses here:
A neural modelling approach to investigating general intelligence
Hierarchical reinforcement learning in a biologically plausible neural architecture
Prior to that I completed my undergrad at Mount Allison University in Sackville, NB. I graduated in 2008 with a double major in Computer Science and Philosophy.
Research interests
I am interested in general intelligence: what is it, how does it develop, what are its components, and how might we model it. I am particularly interested in incorporating large scale adaptability into these models, so that we can understand how they come to be and how they develop into the future. This has led to my interest in reinforcement learning, where I seek to understand how we could learn from experience to build up the complex processing of intelligence.
Current projects
Post-doc at Princeton University
Past projects
Building neural models capable of hierarchical reinforcement learning
Here is a brief, high-level presentation I gave describing this research:
Modelling problem solving in Raven's Progressive Matrices
Publications
Theses
- (2014) Hierarchical reinforcement learning in a biologically plausible neural architecture. PhD thesis, University of Waterloo. Abstract PDF External link
- (2010) A neural modelling approach to investigating general intelligence. Master's thesis, University of Waterloo, Waterloo, ON. Abstract PDF External link
Journal Articles
- (2019) NengoDL: Combining deep learning and neuromorphic modelling methods. Neuroinformatics, pages 1–18. Abstract PDF External link
- (2017) A neural model of hierarchical reinforcement learning. PLoS ONE, 12(7):1–39. Abstract PDF DOI
- (2014) A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices. Intelligence, 42:53–82. Abstract DOI External link
- (2014) Nengo: A Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics. Abstract PDF DOI External link
- (2013) God, the devil, and details: Fleshing out the predictive processing framework (commentary on Clark). Behavioral and Brain Sciences, 36:223-224. Abstract External link
- (2013) Modeling brain function: Current developments and future prospects. JAMA Neurology, 70(10):1325–1329. Abstract DOI External link
- (2012) A large-scale model of the functioning brain. Science, 338:1202-1205. Abstract PDF Poster DOI External link
- (2011) A neural model of rule generation in inductive reasoning. Topics in Cognitive Science, 3:140-153. Abstract PDF External link
Conference and Workshop Papers
- (2015) Spaun: A biologically realistic large-scale functional brain model. In Ontario and Canada Research Chairs Symposium. Council of Ontario Universities. Abstract PDF
- (2014) A neural model of hierarchical reinforcement learning. In Paul Bello, Marcello Guarini, Marjorie McShane, and Brian Scassellati, editors, Proceedings of the 36th Annual Conference of the Cognitive Science Society, 1252–1257. Austin. Cognitive Science Society. Abstract PDF External link
- (2013) A neural reinforcement learning model for tasks with unknown time delays. In 35th Annual Conference of the Cognitive Science Society, 3257–3262. Abstract PDF Poster
- (2013) Spaun: A Large-Scale Model of the Functioning Brain. In Cheriton Symposium. David R. Cheriton School of Computer Science. Abstract PDF
- (2010) A neural model of rule generation in inductive reasoning. In Richard Cattrambone and Stellan Ohlsson, editors, 32nd Annual Conference of the Cognitive Science Society. Portland, OR. Cognitive Science Society. Abstract PDF