| 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 | 700 | 2014 |
| Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks AR Voelker, I Kajić, C Eliasmith Advances in Neural Information Processing Systems, 15544-15553, 2019 | 316 | 2019 |
| Braindrop: A mixed-signal neuromorphic architecture with a dynamical systems-based programming model A Neckar, S Fok, BV Benjamin, TC Stewart, NN Oza, AR Voelker, ... Proceedings of the IEEE 107 (1), 144-164, 2018 | 280 | 2018 |
| A neural representation of continuous space using fractional binding B Komer, TC Stewart, AR Voelker, C Eliasmith Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 2019 | 85 | 2019 |
| Human-inspired neurorobotic system for classifying surface textures by touch KE Friedl, AR Voelker, A Peer, C Eliasmith IEEE Robotics and Automation Letters 1 (1), 516-523, 2016 | 80 | 2016 |
| A neural model of hierarchical reinforcement learning D Rasmussen, A Voelker, C Eliasmith PloS one 12 (7), e0180234, 2017 | 78 | 2017 |
| Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells AR Voelker, C Eliasmith Neural computation 30 (3), 569-609, 2018 | 71 | 2018 |
| Efficiently sampling vectors and coordinates from the n-sphere and n-ball AR Voelker, J Gosmann, TC Stewart Centre for Theoretical Neuroscience-Technical Report 1 (2017), 2017 | 39 | 2017 |
| Dynamical systems in spiking neuromorphic hardware AR Voelker University of Waterloo, 2019 | 36 | 2019 |
| Learning large-scale heteroassociative memories in spiking neurons AR Voelker, E Crawford, C Eliasmith Unconventional computation and natural computation 7 (2014), 2014 | 34 | 2014 |
| Simulating and predicting dynamical systems with spatial semantic pointers AR Voelker, P Blouw, X Choo, NSY Dumont, TC Stewart, C Eliasmith Neural Computation 33 (8), 2033-2067, 2021 | 27 | 2021 |
| 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 | 26 | 2020 |
| A solution to the dynamics of the prescribed error sensitivity learning rule AR Voelker Centre for Theoretical Neuroscience, Waterloo, 2015 | 26 | 2015 |
| Hardware aware training for efficient keyword spotting on general purpose and specialized hardware P Blouw, G Malik, B Morcos, AR Voelker, C Eliasmith arXiv preprint arXiv:2009.04465, 2020 | 25 | 2020 |
| Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework J Knight, AR Voelker, A Mundy, C Eliasmith, S Furber 2016 International Joint Conference on Neural Networks (IJCNN), 5210-5217, 2016 | 25 | 2016 |
| Extending the Neural Engineering Framework for Nonideal Silicon Synapses AR Voelker, BV Benjamin, TC Stewart, K Boahen, C Eliasmith IEEE International Symposium on Circuits and Systems (ISCAS), 2017 | 19 | 2017 |
| Representing spatial relations with fractional binding T Lu, AR Voelker, B Komer, C Eliasmith Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 2019 | 17 | 2019 |
| A Population-Level Approach to Temperature Robustness in Neuromorphic Systems E Kauderer-Abrams, A Gilbert, AR Voelker, B Benjamin, TC Stewart, ... IEEE International Symposium on Circuits and Systems (ISCAS), 2017 | 17 | 2017 |
| A Spiking Independent Accumulator Model for Winner-Take-All Computation J Gosmann, AR Voelker, C Eliasmith Proceedings of the 39th Annual Conference of the Cognitive Science Society, 2017 | 16 | 2017 |
| Methods and systems for implementing dynamic neural networks AR VOELKER, CD Eliasmith US Patent 11,238,337, 2022 | 15 | 2022 |
Chris EliasmithProfessor, University of WaterlooVerified email at uwaterloo.ca
