Daniel Rasmussen
Daniel Rasmussen
Applied Brain Research
Verified email at appliedbrainresearch.com - Homepage
Cited by
Cited by
A large-scale model of the functioning brain
C Eliasmith, TC Stewart, X Choo, T Bekolay, T DeWolf, C Tang, ...
Science 338 (6111), 1202-1205, 2012
Nengo: A Python tool for building large-scale functional brain models
T Bekolay, J Bergstra, E Hunsberger, T DeWolf, TC Stewart, ...
Frontiers in Neuroinformatics 7, 48, 2013
A neural model of rule generation in inductive reasoning
D Rasmussen, C Eliasmith
Topics in Cognitive Science 3 (1), 140-153, 2011
A neural model of hierarchical reinforcement learning
D Rasmussen, A Voelker, C Eliasmith
PloS one 12 (7), 2017
A neural model of hierarchical reinforcement learning
D Rasmussen, C Eliasmith
36th Annual Conference of the Cognitive Science Society, 1252-1257, 2014
NengoDL: Combining deep learning and neuromorphic modelling methods
D Rasmussen
arXiv preprint arXiv:1805.11144, 2018
A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices
D Rasmussen, C Eliasmith
Intelligence 42, 53-82, 2014
Methods and systems for artificial cognition
CD Eliasmith, TC Stewart, FX Choo, TW Bekolay, T Crncich-DeWolf, ...
US Patent 20140156577A1, 2013
God, the devil, and the details: Fleshing out the predictive processing framework
D Rasmussen, C Eliasmith
Behavioral and Brain Sciences 36 (3), 223-224, 2013
Modeling brain function: Current developments and future prospects
D Rasmussen, C Eliasmith
JAMA Neurology 70 (10), 1325-1329, 2013
A neural reinforcement learning model for tasks with unknown time delays
D Rasmussen, C Eliasmith
35th Annual Conference of the Cognitive Science Society, 3257-3262, 2013
A spike in performance: Training hybrid-spiking neural networks with quantized activation functions
AR Voelker, D Rasmussen, C Eliasmith
arXiv preprint arXiv:2002.03553, 2020
A neural modelling approach to investigating general intelligence
D Rasmussen
University of Waterloo, 2010
Hierarchical reinforcement learning in a biologically plausible neural architecture
D Rasmussen
University of Waterloo, 2014
Methods and systems for performing reinforcement learning in hierarchical and temporally extended environments
DH Rasmussen, CD Eliasmith
US Patent App. 14/836,084, 2015
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