I started my masters in Computer Science at the lab in September 2013, and fast-tracked towards a PhD in September 2014. I successfully defended my PhD, titled "Dynamical Systems in Spiking Neuromorphic Hardware", in April 2019. I received my BMath Double Honours in Computer Science and Combinatorics & Optimization from the University of Waterloo, have worked at Amazon, Google, and Wish, and currently work for Applied Brain Research Inc.
My thesis addresses our understanding of how the brain can effectively represent and process dynamic stimuli, while using only spikes to transmit information between its billions of neurons. The Neural Engineering Framework (NEF) is a perfect fit for exploring the range of ways this can be accomplished using biologically plausible spiking neurons and synapses. We have extended the NEF to account for more detailed models of neurons and synapses, in order to deploy the NEF on neuromorphic architectures such as Braindrop and Loihi, and to better understand the class of computations made available in neural substrate. Together, this understanding is being used to build large-scale models that process dynamic stimuli online into structured representations, for use in classification, association, and prediction.
Publications
Theses
Books and Book Chapters
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Aaron R. Voelker,
Chris Eliasmith
(2021)
Programming Neuromorphics Using the Neural Engineering Framework.
Nitish V. Thakor, editor. Programming Neuromorphics Using the Neural Engineering Framework, pages 1–43. Springer Singapore, Singapore.
Abstract
PDF
DOI
Journal Articles
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Aaron R. Voelker,
Peter Blouw,
Xuan Choo,
Nicole Sandra-Yaffa Dumont,
Terrence C. Stewart,
Chris Eliasmith
(2021)
Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers.
Neural Computation, 33(8):2033-2067.
Abstract
PDF
DOI
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Alexander Neckar,
Sam Fok,
Ben V. Benjamin,
Terrence C. Stewart,
Nick N. Oza,
Aaron R. Voelker,
Chris Eliasmith,
Rajit Manohar,
Kwabena Boahen
(2019)
Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model.
Proceedings of the IEEE, 107:144–164.
Abstract
PDF
DOI
External link
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Aaron R. Voelker,
Chris Eliasmith
(2018)
Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.
Neural Computation, 30(3):569-609.
Abstract
PDF
DOI
External link
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Daniel Rasmussen,
Aaron R. Voelker,
Chris Eliasmith
(2017)
A neural model of hierarchical reinforcement learning.
PLoS ONE, 12(7):1–39.
Abstract
PDF
DOI
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Ken Elmar Friedl,
Aaron R. Voelker,
Angelika Peer,
Chris Eliasmith
(2016)
Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch.
Robotics and Automation Letters, 1(1):516-523.
Abstract
PDF
DOI
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Sean Aubin,
Aaron R. Voelker,
Chris Eliasmith
(2016)
Improving With Practice: A Neural Model of Mathematical Development.
Topics in Cognitive Science.
Abstract
PDF
DOI
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Trevor Bekolay,
James Bergstra,
Eric Hunsberger,
Travis DeWolf,
Terrence C Stewart,
Daniel Rasmussen,
Xuan Choo,
Aaron R. Voelker,
Chris Eliasmith
(2014)
Nengo: A Python tool for building large-scale functional brain models.
Frontiers in Neuroinformatics.
Abstract
PDF
DOI
External link
Conference and Workshop Papers
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Peter Blouw,
Gurshaant Malik,
Benjamin Morcos,
Aaron Voelker,
Chris Eliasmith
(2021)
Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware.
In TinyML Research Symposium.
Abstract
arXiv
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Joost de Jong,
Aaron Voelker,
Terry Stewart,
Chris Eliasmith,
Heddrick van Rijn
(2021)
A neurocomputational model of prospective and retrospective timing.
In International Conference on Cognitive Modeling. Toronto, ON. Society for Mathematical Psychology.
Abstract
External link
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Joost de Jong,
Aaron R. Voelker,
Hedderik van Rijn,
Terrence C. Stewart,
Chris Eliasmith
(2019)
Flexible Timing with Delay Networks – The Scalar Property and Neural Scaling.
In 17th Annual Meeting of the International Conference on Cognitive Modelling (ICCM).
Abstract
PDF
External link
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Joost de Jong,
Aaron R. Voelker,
Hedderik van Rijn,
Terrence C. Stewart,
Chris Eliasmith
(2019)
A Neurocomputational Account of Ecologically Plausible, Flexible Timing with Legendre Memory.
In 2nd Annual Conference of the Timing Research Forum. Querétaro, México.
Abstract
Poster
External link
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Aaron R. Voelker,
Ivana Kajić,
Chris Eliasmith
(2019)
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks.
In Advances in Neural Information Processing Systems, 15544–15553.
Abstract
PDF
Poster
External link
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Brent Komer,
Terrence C. Stewart,
Aaron R. Voelker,
Chris Eliasmith
(2019)
A neural representation of continuous space using fractional binding.
In 41st Annual Meeting of the Cognitive Science Society. Montreal, QC. Cognitive Science Society.
Abstract
PDF
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Thomas Lu,
Aaron R. Voelker,
Brent Komer,
Chris Eliasmith
(2019)
Representing spatial relations with fractional binding.
In 41st Annual Meeting of the Cognitive Science Society. Montreal, QC. Cognitive Science Society.
Abstract
PDF
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Andreas Stöckel,
Aaron R. Voelker,
Chris Eliasmith
(2018)
Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework.
In Cosyne Abstracts. Denver USA.
Abstract
PDF
Poster
External link
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Eric Kauderer-Abrams,
Andrew Gilbert,
Aaron R. Voelker,
Ben V. Benjamin,
Terrence C. Stewart,
Kwabena Boahen
(2017)
A Population-Level Approach to Temperature Robustness in Neuromorphic Systems.
In IEEE International Symposium on Circuits and Systems (ISCAS). Baltimore, MD. IEEE.
Abstract
PDF
External link
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Jan Gosmann,
Aaron R. Voelker,
Chris Eliasmith
(2017)
A Spiking Independent Accumulator Model for Winner-Take-All Computation.
In Proceedings of the 39th Annual Conference of the Cognitive Science Society. London, UK. Cognitive Science Society.
Abstract
PDF
Poster
External link
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Sugandha Sharma,
Aaron R. Voelker,
Chris Eliasmith
(2017)
A Spiking Neural Bayesian Model of Life Span Inference.
In Proceedings of the 39th Annual Conference of the Cognitive Science Society. London, UK. Cognitive Science Society.
Abstract
PDF
External link
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Aaron R. Voelker,
Ben V. Benjamin,
Terrence C. Stewart,
Kwabena Boahen,
Chris Eliasmith
(2017)
Extending the Neural Engineering Framework for Nonideal Silicon Synapses.
In IEEE International Symposium on Circuits and Systems (ISCAS). Baltimore, MD. IEEE.
Abstract
PDF
Poster
External link
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Sean Aubin,
Aaron R. Voelker,
Chris Eliasmith
(2016)
Improving with Practice: A Neural Model of Mathematical Development.
In Anna Papafragou Dan Grodner, Dan Mirman and John Trueswell, editors, Proceedings of the 38th Annual Conference of the Cognitive Science Society, 2012–2026. Philadelphia, Pennsylvania. Cognitive Science Society.
Abstract
PDF
External link
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James Knight,
Aaron R. Voelker,
Andrew Mundy,
Chris Eliasmith,
Steve Furber
(2016)
Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework.
In The 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver, British Columbia. IEEE.
Abstract
External link
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Aaron R. Voelker,
Chris Eliasmith
(2015)
Computing with temporal representations using recurrently connected populations of spiking neurons.
In Connecting Network Architecture and Network Computation. Banff International Research Station for Mathematical Innovation and Discovery.
Abstract
DOI
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Aaron R. Voelker,
Eric Crawford,
Chris Eliasmith
(2014)
Learning large-scale heteroassociative memories in spiking neurons.
In Steffen Kopecki Oscar H. Ibarra, Lila Kari, editor, Unconventional Computation and Natural Computation. London, Ontario. Springer International Publishing.
Abstract
PDF
Poster
Technical Reports and Preprints
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Narsimha Chilkuri,
Eric Hunsberger,
Aaron Voelker,
Gurshaant Malik,
Chris Eliasmith
(2021)
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers.
arXiv preprint.
Abstract
PDF
arXiv
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Aaron R. Voelker,
Daniel Rasmussen,
Chris Eliasmith
(2020)
A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions.
arxiv.
Abstract
PDF
DOI
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Aaron R. Voelker,
Jan Gosmann,
Terrence C. Stewart
(2017)
Efficiently sampling vectors and coordinates from the n-sphere and n-ball.
Technical Report, Centre for Theoretical Neuroscience, Waterloo, ON.
Abstract
PDF
DOI
External link
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Aaron R. Voelker,
Chris Eliasmith
(2017)
Methods for applying the Neural Engineering Framework to neuromorphic hardware.
arXiv preprint arXiv:1708.08133.
Abstract
PDF
arXiv
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Aaron R. Voelker,
Chris Eliasmith
(2017)
Analysis of oscillatory weight changes from online learning with filtered spiking feedback.
Technical Report, Centre for Theoretical Neuroscience, Waterloo, ON.
Abstract
DOI
External link
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Andreas Stöckel,
Aaron R. Voelker,
Chris Eliasmith
(2017)
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework.
arXiv preprint arXiv:1710.07659.
Abstract
PDF
arXiv
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Aaron R. Voelker
(2015)
A Solution to the Dynamics of the Prescribed Error Sensitivity Learning Rule.
Technical Report, Centre for Theoretical Neuroscience, Waterloo, ON.
Abstract
PDF
DOI
External link