© 2025 All rights reserved.

Publications

  1. M Bartlett, PM Furlong, T Stewart, J Orchard, E Robinson, M Jackson, “Dissociable Effects of Methylphenidate and Amphetamine on Learning Rate and Reward-Prediction Error”, The Mechanistic Basis of Foraging Conference, (poster, accepted), Nov 2025.

  2. M Bartlett, Furlong, Stewart, J Orchard, “A Computational Model of Action Specification in the Basal Ganglia”, bioRxiv:2025.08.12.669938, (30 pages), Aug 2025.

  3. W Pugsley, J Zheng, J Orchard, R Itier, “A Mechanistic Perspective of Face Perception Latency: Predictive Coding”, Proc. CogSci, pages 3228-3234, July 2025.

  4. E Ganjidoost, J Orchard, “Enhancing Predictive Coding Networks for Multi-Modal Generation and Classification”, Canadian Conference on Artificial Intelligence, (12 pages), May 2025.

  5. A Bremer, J Orchard, “Improved Cleanup and Decoding of Fractional Power Encodings”, Conf. on Neuro-Inspired Computational Elements (NICE), (9 pages), Heidelberg, Germany, March 2025.

  6. N Dumont, “Symbols, Dynamics, and Maps: A Neurosymbolic Approach to Spatial Cognition”, PhD thesis, University of Waterloo, March 2025.

  7. N. Shaw, PM Furlong, B Anderson, J Orchard, “Developing a foundation of Vector Symbolic Architectures using category theory”, arXiv:2501.05368, 2025.

  8. PM Furlong, K Simone, N Dumont, M Bartlett, TC Stewart, J Orchard, C Eliasmith, “Biologically-Plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions”, Proc. International Conference on Artificial Neural Networks (LNCS 15019), pp. 94-108, Sept. 2024. (Best paper award)

  9. J Orchard, PM Furlong, K Simone, “Efficient Hyperdimensional Computing with Spiking Phasors”, Neural Computation, 36(9): 1886-1911, Sept 2024.

  10. M Bartlett, PM Furlong, TC Stewart, J Orchard, “Towards Continuous Action Representation in Models of the Basal Ganglia”, UK Neural Computation Conference, (poster), July 2024.

  11. E Ganjidoost, J Orchard, “How Predictive Coding Rescues Feed-Forward Networks on Adversarial Attacks”, Proc. Cognitive Comp. Neurosci., (3 pages), 2024.

  12. M Bartlett, PM Furlong, J Orchard, TC Stewart, “Using Vector Symbolic Architectures for Distributed Action Representations in a Spiking Model of the Basal Ganglia”, Proc. CogSci, (8 pages), 2024.

  13. E Ganjidoost, J Orchard, “Toward a Model of Associative Memory via Predictive Coding”, Proc. Canadian AI, (12 pages), 2024.

  14. J Zheng, J Orchard, “Humans Don’t Get Fooled: Does Predictive Coding Defend Against Adversarial Attack?”, Proc. Canadian AI, (6 pages), 2024.

  15. PM Furlong, N Dumont, J Orchard, “A Recurrent Dynamic Model for Efficient Bayesian Optimization”, Proc. NICE, (5 pages), 2024.

  16. K Simone, TC Stewart, J Orchard, “Novelty detection by density estimation in the fruit fly olfactory circuit”, Computational and Systems Neuroscience conference (COSYNE), abstract & poster, 2024.

  17. TC Stewart, PM Furlong, K Simone, M Bartlett, J Orchard, “Novelty Detection, Insect Olfaction, Mismatch Negativity, and the Representation of Probability in the Brain”, 7 pages, ICCM, Amsterdam, 2023.

  18. PM Furlong, M Bartlett, TC Stewart, C Eliasmith, “Single neuron distribution modelling for anomaly detection and evidence integration”, 7 pages, ICCM, Amsterdam, 2023.

  19. M Bartlett, K Simone, N Dumont, PM Furlong, C Eliasmith, J Orchard, T Stewart, “Improving Reinforcement Learning with Biologically Motivated Continuous State Representations”, 7 pages, ICCM, Amsterdam, 2023.

  20. Dumont, N. S. Y., Furlong, P. M., Orchard, J., & Eliasmith, C. “Exploiting semantic information in a spiking neural SLAM system”, Frontiers in Neuroscience, 17, 2023.

  21. J Orchard, R Jarvis, “Hyperdimensional Computing with Spiking-Phasor Neurons”, Proc. International Conference on Neuromorphic Systems, Santa Fe, NM, pages 150-157, 2023.

  22. A Safron, Z Sheikhbahaee, N Hay, J Orchard, J Hoey, “Value Cores for Inner and Outer Alignment: Simulating Personality Formation via Iterated Policy Selection and Preference Learning with Self-World Modeling Active Inference Agents”, International Workshop on Active Inference, Sept 2022.

  23. M Bartlett, TC Stewart, J Orchard, “Fast Online Reinforcement Learning with Biologically-Based State Representations”, International Conference on Cognitive Modeling, Toronto, July 2022.

  24. M Bartlett, NSY Dumont, PM Furlong, TC Stewart, “Biologically-Plausible Memory for Continuous-Time Reinforcement Learning”, International Conference on Cognitive Modeling, Toronto, July 2022.

  25. M Bartlett, J Orchard, TC Stewart, “Biologically-Based Neural Representations Enable Fast Online Shallow Reinforcement Learning”, Proc. CogSci, 2981-2987, Toronto, July 2022.

  26. M Snow, J Orchard, “Biological Softmax: Demonstrated in Modern Hopfield Networks”, Proc. CogSci, 505-511, Toronto, July 2022.

  27. N Dumont, J Orchard, C Eliasmith, “A model of path integration that connects neural and symbolic representation”, Proc. CogSci, 3662-3668, Toronto, July 2022.

  28. P Torabian, R Pradeep, J Orchard, B Tripp, “Comparison of Foveated Downsampling Techniques in Image Recognition”, Conference on Vision and Intelligent Systems (CVIS), Waterloo, Nov 2020.

  29. W Sun, J Orchard, “A Predictive-Coding Network That Is Both Discriminative and Generative”, Neural Computation, 32(10):1836-1862, October 2020. (pdf)

  30. L Wang, J Zheng, J Orchard, “Evolving Generalized Modulatory Learning: Unifying Neuromodulation and Synaptic Plasticity”, IEEE Transactions on Cognitive and Developmental Systems, 12(4):797-808, 2020. (pdf)

  31. N Shaw, T Jackson, J Orchard, “Biological batch normalization: How intrinsic plasticity improves learning in deep neural networks”, PLoS ONE, 15(9):e0238454, Sept 2020.

  32. R Cody, B Tolson, J Orchard, “Detecting Leaks in Water Distribution Pipes Using a Deep Autoencoder and Hydroacoustic Spectrograms”, Journal of Computing in Civil Engineering, 34(2):04020001, 2020.

  33. J Orchard, W Sun, N Liu, “Why Aren’t All Predictive Coding Networks Generative?”, NeurIPS Workshop on Perception as Generative Reasoning, 6 pages, Vancouver, Dec 2019.

  34. L Wang, J Orchard, “Investigating the Evolution of a Neural Neuroplasticity Learning Rule”, IEEE Trans. Systems, Man, and Cybernetics - Systems, 49(10):2131-2143, Sept 2019.

  35. A Anjum, F Sun, L Wang, J Orchard, “A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming”, International Conference on Artificial Neural Networks: Deep Learning, LNCS 11728, 373-386, Sept. 2019

  36. HA Leopold, J Orchard, J S Zelek, V Lakshminarayanan, “PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation”, Journal of Imaging, 5(2), article 26 (16 pages), 2019.

  37. T Jackson, J Orchard, “Learning in Energy Networks by Minimizing Network Strain”, Montreal Artificial Intelligence and Neuroscience Conference, 1-page abstract, Dec 2018.

  38. A Khan, J Orchard, “Bidirectional Equilibrium Propagation”, Montreal Artificial Intelligence and Neuroscience Conference, 1-page abstract, Dec 2018. Selected for oral presentation

  39. D Xu, A Clappison, C Seth, J. Orchard, “Symmetric Predictive Estimator for Biologically Plausible Neural Learning”, IEEE Trans. Neural Networks and Learning Systems, 29(9):4140-4151, Sept 2018.

  40. S Shahir, B Semnani, G Rafi, J Orchard, S Safavi-Naeini, “Millimeter-wave Multi-view Planar Near-field Scattering Tomography System”, IET Microwaves, Antennas and Propagation, 12(6):858-863, 2018.

  41. R Wiyatno, J Orchard, “Style Memory: Making a Classifier Network Generative”, International Conference on Cognitive Informatics and Cognitive Computing, 6 pages, July 2018.

  42. E Hunsberger, J Orchard, V Reyesos, B Tripp, “Feature-based resource allocation for real-time stereo disparity estimation”, IEEE Access, 5(1):11645-11657, Dec 2017.

  43. L Wang, B Yang, Y Chen, X Zhang, J Orchard, “Improving Neural-Network Classifiers using Nearest Neighbor Partitioning”, IEEE Transactions on Neural Networks and Learning Systems, 28(10):2255-2267, 2017.

  44. J Orchard, L Castricato, “Combatting Adversarial Inputs using a Predictive-Estimator Network”, International Conference on Neural Information Processing, 7 pages, Nov 2017. (Best paper award)

  45. P Wu, J Orchard, “Using Flexible Neural Trees to Seed Backpropagation”, International Conference on Neural Information Processing, 8 pages, Nov 2017.

  46. H Leopold, J Orchard, J Zelek, V Lakshminarayanan, “Segmentation and Feature Extraction of Retinal Vascular Morphology”, SPIE Medical Imaging, February 2017.

  47. H Leopold, J Orchard, J Zelek, V Lakshminarayanan, “Use of Gabor filters and Deep Networks in the Segmentation of Retina Vessel Morphology”, SPIE BiOS, (7 pages), January 2017.

  48. L Wang, B Yang, J Orchard, “Particle Swarm Optimization Using Dynamic Tournament Topology”, Applied Soft Computing, 48:584-596, 2016.

  49. L Wang, J Orchard, B Yang, A Abraham, “Improving Gene Expression Programming using Diversity Preservation Tournament and Its Application in Grid Cell Modeling”, IEEE International Conference on Systems, Man, and Cybernetics, (6 pages), 2016.

  50. J Orchard, L Wang, “The Evolution of a Generalized Neural Learning Rule”, IEEE World Congress on Computational Intelligence (International Joint Conference on Neural Networks), (7 pages), 2016.

  51. H Leopold, J Orchard, V Lakshminarayanan, J Zelek, “A Deep Learning Network for Segmenting Retinal Vessel Morphology”, Proc. of the IEEE Engineering in Medicine and Biology Society Conference, paper 2973 (1 page abstract), 2016.

  52. L Wang, B Yang, J Orchard, “Discovering Grid-Cell Models Through Evolutionary Computation”, IEEE World Congress on Computational Intelligence (IEEE Congress on Evolutionary Computation), (8 pages), 2016.

  53. D Xu, C Seth, J Orchard, “Versatile predictive estimator without weight copying”, Computational and Systems Neuroscience conference (COSYNE), abstract & poster, 2016.

  54. J Orchard, “Oscillator-Interference Models of Path Integration Do Not Require Theta Oscillations”, Neural Computation, 27:548-560, March 2015. (pdf)

  55. S Hu, J Orchard, “Medical Image Ensemble Registration Based on Gaussian Mixture Model and Color Component Regularization”, Optik - International Journal for Light and Electron Optics, 126(1), pp. 6-12, Jan 2015.

  56. S Shahir, J Orchard, S Safavi-Naeini, “Towards Five-Dimensional Imaging Using Near-Field Scattering Tomography System”, Annual Biophysical Society Meeting of Canada, 2015.

  57. S Shahir, J Orchard, S Safavi-Naeini, “Monte Carlo based Non-radiating Objective Function Minimization for Permittivity Profile Estimation”, IEEE Antennas and Propagation, 2-page abstract, July 2015.

  58. S Shahir, J Orchard, S Safavi-Naeini, “Scatterer Localization Based on the Non-Radiating Equivalent Source (2D Case)”, IEEE Antennas and Propagation, 2-page abstract, July 2014.

  59. S Shahir, A Taeb, G Rafi, J Orchard, S Safavi-Naeini, “Electromagnetic Inverse Scattering Based Object Imaging and Characterization”, URSI International Union of Radio Science, 1-page abstract, July 2014.

  60. J Orchard, H Yang, X Ji, “Does the Entorhinal Cortex use the Fourier Transform?”, Frontiers in Computational Neuroscience, vol. 7, article 179 (14 pages), Dec. 2013.

  61. J Orchard, H Yang, X Ji, “Path Integration using the Fourier Transform”, Berstein Conference, abstract & poster, Sept 2013.

  62. S Shahir, M Mohajer, J Orchard, S Safavi-Naeini, “Electromagnetic Inverse Scattering System Characterization based on Green’s Function Analysis”, IEEE Symposium on Antennas and Propagation, 2 pages, July 2013.

  63. J Orchard , H Yang, X Ji, “Navigation by Path Integration and the Fourier Transform: A Spiking-Neuron Model”, Canadian Conference on Artificial Intelligence (AI), LNAI 7884, pp. 138-149, May 2013. (pdf)

  64. X Ji, S Kushagra, J Orchard, “Sensory Updates to Combat Path-Integration Drift”, Canadian Conference on Artificial Intelligence (AI), LNAI 7884, pp. 263-270, May 2013. (pdf)

  65. J Orchard, R Bogacz, “Decision-Making Networks Using Spiking Neurons”, Computational and Systems Neuroscience conference (COSYNE), abstract & poster, 2013.

  66. BP Tripp, J Orchard, “Population Coding in Sparsely Connected Networks of Noisy Neurons”, Frontiers in Computational Neuroscience, vol. 6, article 23 (14 pages), May 2012.

  67. J Orchard, H-Y Kim, JTW Yeow, “Plausibility of Image Reconstruction Using a Proposed Flexible and Portable CT Scanner”, The Open Medical Imaging Journal, 6, pp. 1-11, April 2012.

  68. M Lam, J Orchard, “Mammalian-like Visual Learning by Spatially Modulating Learning Rate in Deep Belief Networks”, Computational and Systems Neuroscience conference (COSYNE), 1-page abstract & poster, Feb 2011.

  69. J Orchard, R Mann, “Registering a Multi-Sensor Ensemble of Images”, IEEE Transactions on Image Processing, 19(5):1236-1247, 2010.

  70. H-Y Kim, J Orchard, “Registering a Non-Rigid Multi-Sensor Ensemble of Images”, IEEE Engineering in Medicine and Biology Conference (EMBC), 4 pages, 2010.

  71. Y Wang, J Orchard, “Fast Discrete Orthonormal Stockwell Transform”, SIAM Journal on Scientific Computing, 31(5):4000-4012, 2009.

  72. A Wong, J Orchard, “Robust Multimodal Registration using Local Phase Coherence Representations”, Journal of Signal Processing Systems for Signal, Image, and Video Technology: Special Issue on Biomedical Imaging, 54:89-100, 2009.

  73. Y Wang, J Orchard, “Use of the Discrete Orthonormal Stockwell Transform for Image Restoration”, IEEE International Conference on Image Processing (ICIP), pp. 2761-2764, 2009.

  74. J Orchard, L Jonchery, “Ensemble Registration: Aligning Many Multisensor Images Simultaneously”, SPIE Electronic Imaging Conference, 12 pages, Jan 2009.

  75. Y Wang, J Orchard, “A New Image Compression Method using the Stockwell Transform”, SPIE Electronic Imaging Conference, 12 pages, Jan 2009.

  76. A Wong, J Orchard, “Efficient FFT-Accelerated Approach to Invariant Optical-LIDAR Registration”, IEEE Transactions on Geoscience and Remote Sensing, 46(11):3917-3925, Nov. 2008.

  77. M Omanovic, J Orchard, “Exhaustive Matching of Dental X-rays for Human Forensic Identification”, Canadian Society for Forensic Science Journal, 41(3):8 pages, Sept. 2008.

  78. Y Wang, J Orchard, “Symmetric Discrete Orthonormal Stockwell Transform”, International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), 5 pages, Sept 2008.

  79. J Orchard, JTW Yeow, “Toward a Flexible and Portable CT Scanner”, Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS 5242, pp. 188-195, Sept 2008.

  80. J Orchard, “Multimodal Image Registration using Floating Regressors in the Joint Intensity Scatter Plot”, Medical Image Analysis, 12(4):385-396, August 2008.

  81. J Orchard, M Ebrahimi, A Wong, “Efficient Nonlocal-Means Denoising using the SVD”, IEEE International Conference on Image Processing (ICIP’08), 4 pages, 2008.

  82. A Wong, J Orchard, “A Nonlocal-Means Approach to Exemplar-Based Inpainting”, IEEE International Conference on Image Processing (ICIP’08), 4 pages, 2008.

  83. J Orchard, C Kaplan, “Cut-Out Image Mosaics”, Proc. of the 6th Symposium on Non-Photorealistic Animation and Rendering (NPAR), pp. 79-87, June 2008. (one of our images was featured on the cover of the proceedings)

  84. R Sarkar, C de Almeida, N Syed, S Jamal, J Orchard, “Intuitive Interface for the Exploration of Volumetric Datasets”, Proc. of the International Conference on Systems, Computing Sciences and Software Engineering, 6 pages, Dec 2007.

  85. J Orchard, “Efficient Least-Squares Multimodal Registration with a Globally Exhaustive Alignment Search”, IEEE Transactions on Imaging Processing, 16(10):2526-2534, October 2007.

  86. J Orchard, “Globally Optimal Multimodal Rigid Registration: An Analytic Solution Using Edge Information”, IEEE International Conference on Image Processing (ICIP’07), 4 pages, 2007.

  87. J Orchard, A Ramotar, “Autocorrecting Reconstruction for Flexible CT Scanners”, Proc. of IEEE International Symposium on Biomedical Imaging (ISBI’07), pp. 804-807, April 2007.

  88. A Wong, J Orchard, “Efficient and Robust Non-Rigid Least-Squares Rectification of Medical Images”, Proc. of the International Conference on Image Processing and Computer Vision (IPCV’06), pp. 67-73, 2006.

  89. A Wong, W Bishop, J Orchard, “Efficient Multi-Modal Least-Squares Alignment of Medical Images Using Quasi-Orientation Maps”, Proc. of the International Conference on Image Processing and Computer Vision (IPCV’06), pp. 74-80, 2006.

  90. M Omanovic, J Orchard, “Efficient Multimodal Registration using Least-Squares”, Proc. of the International Conference on Image Processing and Computer Vision (IPCV’06), Paper IPC4639, 4 pages, 2006.

  91. A Ramotar, J Orchard, “General Geometry CT Reconstruction”, Proc. of the International Conference on Image Processing and Computer Vision (IPCV’06), pp. 95-99, 2006.

  92. J Orchard, “Efficient Global Weighted Least-Squares Translation Registration in the Frequency Domain”, International Conference on Image Analysis and Recognition (ICIAR’05), LNCS 3656, pp. 116-124, 2005.

  93. J Orchard, “Image Deformation using Velocity Fields: An Exact Solution”, International Conference on Image Analysis and Recognition (ICIAR’05), LNCS 3656, pp. 439-446, 2005.

  94. LM Freire, J Orchard, M Jenkinson, J-F Mangin, “Reducing Activation-related Bias in FMRI Registration”, Proceedings of Medical Imaging and Augmented Reality (MIAR’04), LNCS 3150, pp. 278-285, August 2004.

  95. J Orchard, MS Atkins, “Solving for Motion and Activation Simultaneously in an fMRI Experiment with Multiple Stimulus Conditions”, IEEE International Symposium on Biomedical Imaging, pp. 1000-1004, April 2004.

  96. J Orchard, “Simultaneous Registration and Activation Detection: Overcoming Activation-Induced Registration Errors in Functional MRI”, Ph.D. Thesis, Simon Fraser University, 2003.

  97. J Orchard, MS Atkins, “Iterating Registration and Activation Detection to Overcome Activation Bias in Motion Estimates”, Medical Image Computing and Computer Assisted Intervention 6 (MICCAI), LNCS 2879, pp. 886-893, November 2003.

  98. J Orchard, C Greif, GH Golub, B Bjornson, MS Atkins, “Simultaneous Registration and Activation Detection for fMRI”, IEEE Transactions on Medical Imaging special issue on Medical Image Registration, 22(11):1427-1435, 2003.

  99. J Orchard, C Greif, GH Golub, B Bjornson, MS Atkins, “Overcoming Activation-Induced Registration Errors in fMRI”, Proc. of SPIE Conference on Medical Imaging, pp. 130-138, Feb 2003.

  100. MS Atkins, J Orchard, B Law, MK Tory, “Robustness of the Brain Parenchymal Fraction for Measuring Brain Atrophy”, Proc. of SPIE Conference on Medical Imaging, pp. 201-205, Feb 2002.

  101. MS Atkins, K Siu, B Law, J Orchard, WL Rosenbaum, “Difficulties of T1 Brain Image Segmentation”, Proc. of SPIE Conference on Medical Imaging, pp. 1837-1844, Feb 2002.

  102. MS Atkins, J Orchard, MK Tory, “Evaluation of Brain Atrophy Measures”, Proc. of the IEEE Engineering in Medicine and Biology Society Conference, vol. 1, pp. 616-619, Oct 2001.

  103. J Orchard, T Möller, “Accelerated Splatting using a 3D Adjacency Data Structure”, Graphics Interface Conference, pp. 191-200, June 2001.