| 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 | 987 | 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, 2014 | 488 | 2014 |
| Learning to select actions with spiking neurons in the basal ganglia TC Stewart, T Bekolay, C Eliasmith Frontiers in Neuroscience 6, 2012 | 111 | 2012 |
| Neural representations of compositional structures: Representing and manipulating vector spaces with spiking neurons TC Stewart, T Bekolay, C Eliasmith Connection Science 23 (2), 145-153, 2011 | 76 | 2011 |
| Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks T Bekolay, C Kolbeck, C Eliasmith 35th Annual Conference of the Cognitive Science Society, 169-174, 2013 | 59 | 2013 |
| A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex T Bekolay, M Laubach, C Eliasmith The Journal of Neuroscience 34 (5), 1892-1902, 2014 | 44 | 2014 |
| Methods and systems for artificial cognition CD Eliasmith, TC Stewart, F Choo, TW Bekolay, T Crncich-dewolf, Y Tang, ... US Patent 20,140,156,577, 2014 | 32* | 2014 |
| Reduction of dopamine in basal ganglia and its effects on syllable sequencing in speech: a computer simulation study V Senft, TC Stewart, T Bekolay, C Eliasmith, BJ Kröger Basal Ganglia 6 (1), 7-17, 2016 | 25 | 2016 |
| Modeling interactions between speech production and perception: speech error detection at semantic and phonological levels and the inner speech loop BJ Kröger, E Crawford, T Bekolay, C Eliasmith Frontiers in Computational Neuroscience 10, 51, 2016 | 22 | 2016 |
| Modeling speech production using the Neural Engineering Framework BJ Kröger, T Bekolay, C Eliasmith 2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom), 203-208, 2014 | 16 | 2014 |
| Learning in large-scale spiking neural networks T Bekolay University of Waterloo, 2011 | 16 | 2011 |
| Biologically inspired methods in speech recognition and synthesis: closing the loop T Bekolay University of Waterloo, 2016 | 9 | 2016 |
| Modeling motor planning in speech production using the Neural Engineering Framework BJ Kröger, T Bekolay, P Blouw Electronic Speech Signal Processing (ESSV), 2016 | 9 | 2016 |
| Modeling the mental lexicon as part of long-term and working memory and simulating lexical access in a naming task including semantic and phonological cues CM Stille, T Bekolay, P Blouw, BJ Kröger Frontiers in Psychology 11, 1594, 2020 | 8 | 2020 |
| Neural modeling of speech processing and speech learning BJ Kröger, T Bekolay Springer International Publishing, 2019 | 8 | 2019 |
| Inhibiting basal ganglia regions reduces syllable sequencing errors in Parkinson's disease: a computer simulation study V Senft, TC Stewart, T Bekolay, C Eliasmith, BJ Kröger Frontiers in computational neuroscience 12, 41, 2018 | 7 | 2018 |
| A general error-modulated STDP learning rule applied to reinforcement learning in the basal ganglia T Bekolay, C Eliasmith | 7 | 2011 |
| Hierarchical sequencing and feedforward and feedback control mechanisms in speech production: a preliminary approach for modeling normal and disordered speech BJ Kröger, CM Stille, P Blouw, T Bekolay, TC Stewart Frontiers in Computational Neuroscience 14, 573554, 2020 | 6 | 2020 |
| Learning nonlinear functions on vectors: examples and predictions T Bekolay CTN Technical Report, 2010 | 5 | 2010 |
| Methods and systems for extracting auditory features with neural networks T Bekolay US Patent 10,026,395, 2018 | 4 | 2018 |
Chris EliasmithProfessor, University of WaterlooVerified email at uwaterloo.ca
