Jonathan Timcheck

Publications

B. Meszaros, J. C. Knight, J. Timcheck, and T. Nowotny, “A Complete Pipeline for Deploying SNNs with Synaptic Delays on Loihi 2,” in Proceedings of the International Conference on Neuromorphic Systems (ICONS), 2025. https://doi.org/10.1109/ICONS69015.2025.00041

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A. Pierro, S. Abreu, J. Timcheck, P. Stratmann, A. Wild, and S. B. Shrestha, “Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity,” Forty-second International Conference on Machine Learning (ICML), 2025. https://openreview.net/forum?id=UNrfYfbLZ3

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T. Shoesmith, J. C. Knight, B. Meszaros, J. Timcheck, and T. Nowotny, “Eventprop training for efficient neuromorphic applications,” 2025 Neuro Inspired Computational Elements (NICE), 2025. https://doi.org/10.1109/NICE65350.2025.11064940

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J. Yik, …, J. Timcheck et al., “The neurobench framework for benchmarking neuromorphic computing algorithms and systems,” Nature communications, 2025. https://www.nature.com/articles/s41467-025-56739-4

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S. M. Meyer, …, J. Timcheck et al., “A diagonal structured state space model on loihi 2 for efficient streaming sequence processing,” in 2025 Neuro Inspired Computational Elements (NICE), IEEE, 2025, pp. 1–9. Accessed: Dec. 02, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/11065663/

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S. B. Shrestha, J. Timcheck, P. Frady, L. Campos-Macias, and M. Davies, “Efficient video and audio processing with loihi 2,” 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024. https://ieeexplore.ieee.org/abstract/document/10448003/

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J. Timcheck et al., “The Intel neuromorphic DNS challenge,” Neuromorphic Computing and Engineering, 2023. https://doi.org/10.1088/2634-4386/ace737

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C. Mackin, G. Burr, and J. P. Timcheck, “Translating artificial neural network software weights to hardware-specific analog conductances,” US20230105568A1, 2023. https://patents.google.com/patent/US20230105568A1/en

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J. Timcheck, J. Kadmon, K. Boahen, and S. Ganguli, “Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays,” PLoS computational biology, 2022. https://doi.org/10.1371/journal.pcbi.1010593

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C. Mackin, …, J. Timcheck et al., “Optimised weight programming for analogue memory-based deep neural networks,” Nature communications, 2022. https://www.nature.com/articles/s41467-022-31405-1

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J. Kadmon, J. Timcheck, and S. Ganguli, “Predictive coding in balanced neural networks with noise, chaos and delays,” Advances in neural information processing systems, 2020. https://proceedings.neurips.cc/paper/2020/hash/c236337b043acf93c7df397fdb9082b3-Abstract.html

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C. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan, “Pymt5: multi-mode translation of natural language and python code with transformers,” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. https://aclanthology.org/2020.emnlp-main.728/

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S. Chatrchyan, …, J. Timcheck, et al., “Search for the standard model Higgs boson produced in association with a top-quark pair in pp collisions at the LHC,” Journal of High Energy Physics, 2013. https://doi.org/10.1007/JHEP05(2013)145

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