Towards provably moral AI agents in bottom-up learning frameworks NP Shaw, A Stöckel, RW Orr, TF Lidbetter, R Cohen Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 271-277, 2018 | 28 | 2018 |
Passive nonlinear dendritic interactions as a computational resource in spiking neural networks A Stöckel, C Eliasmith Neural Computation 33 (1), 96-128, 2021 | 20 | 2021 |
Point neurons with conductance-based synapses in the neural engineering framework A Stöckel, AR Voelker, C Eliasmith arXiv preprint arXiv:1710.07659, 2017 | 9 | 2017 |
Ontology-based extraction of structured information from publications on preclinical experiments for spinal cord injury treatments B Paassen, A Stöckel, R Dickfelder, JP Göpfert, N Brazda, T Kirchhoffer, ... Proceedings of the Third Workshop on Semantic Web and Information Extraction …, 2014 | 9 | 2014 |
Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule–Golgi Microcircuit A Stöckel, TC Stewart, C Eliasmith Topics in Cognitive Science 13 (3), 515-533, 2021 | 8 | 2021 |
Binary associative memories as a benchmark for spiking neuromorphic hardware A Stöckel, C Jenzen, M Thies, U Rückert Frontiers in computational neuroscience 11, 71, 2017 | 8 | 2017 |
Harnessing neural dynamics as a computational resource A Stöckel University of Waterloo, 2022 | 6 | 2022 |
Computational properties of multi-compartment LIF neurons with passive dendrites A Stöckel, C Eliasmith Neuromorphic Computing and Engineering 2 (2), 024011, 2022 | 5 | 2022 |
A Biologically Plausible Spiking Neural Model of Eyeblink Conditioning in the Cerebellum A Stöckel, TC Stewart, C Eliasmith CogSci, 2020 | 5 | 2020 |
Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms NSY Dumont, A Stöckel, PM Furlong, M Bartlett, C Eliasmith, TC Stewart Brain Sciences 13 (2), 245, 2023 | 3 | 2023 |
Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework A Stöckel, AR Voelker, C Eliasmith Cosyne Abstracts, 2018 | 3 | 2018 |
Design space exploration of associative memories using spiking neurons with respect to neuromorphic hardware implementations A Stöckel Bielefeld University, 2016 | 3 | 2016 |
Learned legendre predictor: Learning with compressed representaitons for efficient online multistep prediction PM Furlong, A Stöckel, T Stewart, C Eliasmith Technical Report, 2022 | 2 | 2022 |
Discrete function bases and convolutional neural networks A Stöckel arXiv preprint arXiv:2103.05609, 2021 | 2 | 2021 |
Constructing dampened LTI systems generating polynomial bases A Stöckel arXiv preprint arXiv:2103.00051, 2021 | 2 | 2021 |
SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments–A Webservice and Open Source Toolkit A Stöckel, B Paassen, R Dickfelder, JP Göpfert, N Brazda, HW Müller, ... bioRxiv, 013458, 2015 | 2 | 2015 |
Assorted Notes on Radial Basis Functions A Stöckel Waterloo, ON: Centre for Theoretical Neuroscience. doi 10, 2020 | 1 | 2020 |
Methods and systems for learning online to predict time-series data T Stewart, A Stoeckel, C Eliasmith US Patent App. 17/895,910, 2023 | | 2023 |
A Geometric Interpretation of Feedback Alignment. A Stöckel, TC Stewart, C Eliasmith CogSci, 3366, 2019 | | 2019 |
Ousía A Stöckel, B Paassen, P Cimiano | | |