PUBLICATIONS

Journal Publications

2025

  1. C. Arrow, M. Ward, J. K. Eshraghian, G. Dwivedi, “Neck-focused Remote Photoplethysmography (rPPG): A comparative study using clinical data and the PyVHR framework”, Computers in Biology and Medicine, October 2025.
  2. S. Gunasekaran, A. Kembay, H. Ladret, R. J. Zhu, L. Perrinet, O. Kavehei, J. K. Eshraghian, “A predictive approach to enhance time-series forecasting”, Nature communications, September 2025.
  3. Y. Chen, S. S. Yu, Z. Li, J. K. Eshraghian, C. P. Lim, “Interplay between Bayesian Neural Networks and Deep Learning: A Survey”, Knowledge-based Systems, September 2025.
  4. B. Walters, Y. Bethi, T. Kergan, B. Nguyen, A. Amirsoleimani, J. K. Eshraghian, S. Afshar, M. R. Azghadi, “NeuroMorse: A Temporally Structured Dataset for Neuromorphic Computing”, Neuromorphic Computing and Engineering, May 2025.
  5. Z. Zhang, H. Yang, J. Li, S. W. Chong, J. K. Eshraghian, K. Yong, D. Vigolo, H. McGuire, O. Kavehei, “Neuromorphic imaging cytometry on human blood cells”, April 2025.
  6. Z. Chen, et al., “ON-OFF Neuromorphic ISING machines using Fowler-Nordheim annealers”, Nature Communications, March 2025.
  7. S. Gou, J. Fu, Y. Sha, Z. Cao, Z. Guo, J. K. Eshraghian, R. Li, L. Jiao, “Dynamic Spatio-Temporal Pruning for Efficient Spiking Neural Networks”, Frontiers in Neuroscience, March 2025.
  8. M. Karamimanesh, E. Abiri, M. Shahsavari, K. Hassanli, A. van Schaik, J. K. Eshraghian, “Spiking neural networks on FPGA: A survey of methodologies and recent advancements”, Neural Networks, February 2025.
  9. J. Yik et al., “The neurobench framework for benchmarking neuromorphic computing algorithms and systems”, Nature Communications, February 2025.
  10. A. Hess-Dunlop, H. Kakani, S. Taylor, D. Louie, J. K. Eshraghian, C. Josephson, “Time-series forecasting of microbial fuel cell energy generation using deep learning”, Frontiers in Computer Science.

2024

  1. L. F. Herbozo Contreras, N. D. Truong, J. K. Eshraghian, Z. Xu, Z. Huang, Z. Huang, T. Bersani-Vincenzo, I. Aguilar, W. H. Leung, A. Nikpour, O. Kavehei, “Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation”, October 2024.
  2. S. Schmidgall, C. Harris, I. Essien, D. Olshvang, T. Rahman, J. W. Kim, R. Ziaei, J. K. Eshraghian, P. Abadir, R. Chellappa, “Evaluation and mitigation of cognitive biases in medical language models”, npj Digital Medicine, October 2024.
  3. Jens E Pedersen, Steven Abreu, Matthias Jobst, Gregor Lenz, Vittorio Fra, Felix Christian Bauer, Dylan Richard Muir, Peng Zhou, Bernhard Vogginger, Kade Heckel, Gianvito Urgese, Sadasivan Shankar, Terrence C Stewart, Sadique Sheik, Jason K Eshraghian, “Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing”, Nature Communications, September 2024.
  4. B. Walters, H. R. Kalatehbali, Z. Cai, R. Genov, A. Amirsoleimani, J. K. Eshraghian, M. R. Zaghadi, “Efficient sparse spiking auto-encoder for reconstruction, denoising, and classification”, Neuromorphic Computing and Engineering, August 2024.
  5. R. J. Zhu, S. Gunasekaran, J. K. Eshraghian, “Bridging the gap between artificial intelligence and natural intelligence”, Nature Computational Science, August 2024.
  6. R. J. Zhu, Y. Zhang, S. Abreu, E. Sifferman, T. Sheaves, Y. Wang, D. Richmond, S. B. Shrestha, P. Zhou, J.K. Eshraghian, “Scalable MatMul-free Language Modeling on Neuromorphic Hardware”, arXiv preprint arXiv:2406:02528, June 2024.
  7. Z. Zhang, H. Yang, J. K. Eshraghian, J. Li, K.-T. Yong, D. Vigolo, H. M. McGuire, O. Kavehei, “Cell detection with convolutional spiking neural network for neuromorphic cytometry”, APL Machine Learning, June 2024.
  8. Y. Chen, S. Yu, J. K. Eshraghian, C. P. Lim, “Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation”, APL Machine Learning, June 2024.
  9. S. Schmidgall, J. Achterberg, T. Miconi, L. Kirsch, R. Ziaei, S. Hajiseyedrazi, J. K. Eshraghian, “Brain-inspired learning in artificial neural networks: A review”, APL Machine Learning, June 2024.
  10. R. J. Zhu, Z. Wang, L. Gilpin, J. K. Eshraghian, “Autonomous Driving with Spiking Neural Networks”, arXiv preprint arXiv:2405.13672, May 2024.
  11. Y. Shan, M. Zhang, R. Zhu, X. Qiu, J. K. Eshraghian, H. Qu, “Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning”, arXiv preprint:2405.13672, June 2024.
  12. A. Henkes, J. K. Eshraghian, H. Wessels, “Spiking neural network for nonlinear regression”, Royal Society Open Science, May 2024. [pdf]
  13. S. K. Nath, S. K. Das, S. K. Nandi, C. Xi, C. V. Marquez, A. Rúa, M. Uenuma, Z. Wang, S. Zhang, R. Zhu, J. Eshraghian, X. Sun, T. Lu, Y. Bian, N. Syed, W. Pan, H. Wang, W. Lei, L. Fu, L. Faraone, Y. Liu, R. G. Elliman, “Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing”, Advanced Materials, e2400904-e2400904, March 2024.
  14. X. Wang, J. Zhou, C. Dong, C. Jin, J. K. Eshraghian, H. H. C. Iu, S. M. Kang, “A memristor crossbar based on a novel ternary memristor model”, Nonlinear Dynamics, March 2024.
  15. P. V. Sun, A. Titterton, A. Gopiani, T. Santos, A. Basu, W. Lu, J. K. Eshraghian, “Exploiting Deep Learning Accelerators for neuromorphic workloads”, Neuromorphic Computing and Engineering, Jan. 2024.

2023

  1. J. K. Eshraghian, A. Basu, C. Lammie, S. C. Liu, P. Panda, “Guest Editorial: Dynamical Neuro-AI Learning Systems – Devices, Circuits, Architectures, and Algorithms”, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 13(4), pp. 873-876, Dec. 2023.
  2. F. Ottati, C. Gao, Q. Chen, G. Brignone, M. R. Casu, J. K. Eshraghian, L. Lavagno, “To Spike or Not to Spike: A Digital Hardware Perspective on Deep Learning Acceleration”, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 13(4), pp. 1015-1025, Nov. 2023. [pdf]
  3. C. Arrow, M. Ward, J. K. Eshraghian, G. Dwivedi, “Capturing the pulse: A state-of-the-art review on camera-based jugular vein assessment”, Biomedical Optics Express, 14(12), pp. 6470-6492, Dec. 2023.
  4. J. K. Eshraghian, M. G. Ward, E. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, W. D. Lu, “Training Spiking Neural Networks Using Lessons From Deep Learning”, Proceedings of the IEEE, Sep. 2023. [pdf][IEEE]
  5. S. Barchid, J. Mennesson, J. K. Eshraghian, C. Djéraba, M. Bennamoun, “Spiking Neural Networks for Frame-based and Event-based Single Object Localization”, Neurocomputing, Sep. 2023. [pdf]
  6. Z. Zhang, Z. Xu, H. M. McGuire, C. Essam, A. Nicholson, T. Hamilton, J. Li, J. Eshraghian, K. Yong, D. Vigolo, O. Kavehei, “Neuromorphic Cytometry: Implementation on cell counting and size estimation”, Neuromorphic Computing and Engineering, 3(4), Nov. 2023.
  7. J. Lu, J. Stewart, M. Bennamoun, A. Goudie, J. K. Eshraghian, A. Ihdayhid, et al., “Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients”, Computer Methods and Programs in Biomedicine, e. 107717, Sep. 2023.
  8. Z. Wang, Y. Wu, Y. Park, S. Yoo, X. Wang, J. K. Eshraghian, W. D. Lu, “PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators”, Advanced Intelligent Systems, Sep. 2023. [pdf]
  9. J. Lu, M. Bennamoun, J. Stewart, J. K. Eshraghian, Y. Liu, B. Chow, F. M. Sanfilippo, G. Dwivedi, “Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients”, arXiv preprint arXiv:2309.00330, September 2023. [pdf]
  10. L. Fernando Herbozo Contreras, N. D. Truong, J. K. Eshraghian, Z. Xu, Z. Huang, A. Nikpour, O. Kavehei, “Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals”, arXiv preprint arXiv:2307.12471, July 2023. [pdf]
  11. X.-J. Li, X. Wang, P. Li, H. H. C. Iu, J. K. Eshraghian, S. K. Nandi, S. K. Nath, R. G. Elliman, “Tri-State Memristors Based on Composable Discrete Devices”, International Journal of Bifurcation and Chaos, 33(7), June 2023.
  12. A. Mehonic, J. K. Eshraghian, “Brains and bytes: Trends in neuromorphic technology”, APL Machine Learning, 1(2), June 2023.
  13. J. Yik, et al. “NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking”, arXiv preprint, Apr. 2023. [pdf]
  14. Z. Wang, F. Meng, Y. Park, J. K. Eshraghian, W. D. Lu, “Side-channel attack analysis on in-memory computing architectures”, IEEE Transactions on Emerging Topics in Computing, Mar. 2023. [pdf].
  15. R.-J. Zhu, Q. Zhao, J. K. Eshraghian, “SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks”, arXiv preprint, Feb. 2023. [code]
  16. S. Jin, D. Kim, D. Yoo, J. K. Eshraghian, D. S. Jeong, “BPLC + NOSO: Backpropagation of errors based on latency code with neurons that only spike once at most”, Complex & Intelligent Systems, Feb. 2023. [paper][code]
  17. Y. Yang, J. K. Eshraghian, N. D. Truong, A. Nikpour, O. Kavehei, “Neuromorphic Deep Spiking Neural Networks for Seizure Detection”, Neuromorphic Computing and Engineering, Feb. 2023. [pdf]
  18. Y. Li, J. Luo, Q. Dai, J. K. Eshraghian, B. W. Ling, C. Zheng, X. Wang, “A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction”, Biomedical Signal and Control 79(104188). Jan. 2023.
  19. Y Li, L Xie, C Zheng, D Yu, JK Eshraghian, “Modeling and hardware implementation of universal interface-based floating fractional-order mem-elements”, Chaos: An Interdisciplinary Journal of Nonlinear Science, 33 (1), 013141, Jan. 2023.

2022

  1. C. Zheng, L. Peng, J. K. Eshraghian, X. Wang, J. Cen, H. H. C. Iu, “Spiking Neuron Implementation Using a Novel Floating Memcapacitor Emulator”, International Journal of Bifurcation and Chaos, 32 (15), 2250224, Dec. 2022.
  2. P. Zhou, D. Choi, W. D. Lu, S. M. Kang, J. K. Eshraghian, “Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays”, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Nov. 2022. [preprint] [IEEE].
  3. P. S. Sun, A. Titterton, A. Gopiani, T. Santos, A. Basu, W. D. Lu, J. K. Eshraghian, “Intelligence Processing Units Accelerate Neuromorphic Learning”, arXiv preprint arXiv:
    2211.10725, Nov. 2022. [preprint]
  4. M. E. Elbtity, P. S. Chandarana, B. Reidy, J. K. Eshraghian, R. Zand, “APTPU: Approximate Computing Based Tensor Processing Unit”, IEEE Transactions on Circuits and Systems I: Regular Papers. Sep. 2022.
  5. Y. Li, J. Luo, Q. Dai, J. K. Eshraghian, B. W. Ling, C. Zheng, X. Wang, “A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction”, Biomedical Signal and Control 79(104188). Sep. 2022.
  6. Y. Yang, N. D. Truong, J. K. Eshraghian, A. Nikpour, O. Kavehei, “Weak self-supervised learning for seizure forecasting: a feasibility study”, Royal Society Open Science 9 (8), Aug. 2022. [pdf].
  7. C. T. Ngo, J. K. Eshraghian, J. P. Hong, “An Area-Optimized and Power-Efficient CBC-PRESENT and HMAC-PHOTON”, Electronics 11 (15), p. 2380,  Jul. 2022. [pdf].
  8. Y. Yang, N. D. Truong, J. K. Eshraghian, C. Maher, A. Nikpour, O. Kavehei, “A multimodal AI system for out-of-distribution generalization of seizure identification”, IEEE Journal of Biomedical and Health Informatics, 26 (7), pp. 3529 – 3538, Mar. 2022. [pdf].
  9. X. Y. Wang, C. T. Dong, P. F. Zhou, S. K. Nandi, S. K. Nath, R. G. Elliman, H.H.C. Iu, S. M. Kang, J. K. Eshraghian, “Low-Variance Memristor-Based Multi-Level Ternary Combinational Logic”, IEEE Transactions on Circuits and Systems I: Regular Papers 69 (6), 2423-2434, Mar. 2022. [pdf].
  10. J. K. Eshraghian, W. D. Lu, “The fine line between dead neurons and sparsity in binarized spiking neural networks”, arXiv preprint arXiv:2201.11915, Mar. 2022.
  11. J. K. Eshraghian, X. Wang, W. D. Lu, “Memristor-based binarized spiking neural networks: Challenges and applications”, IEEE Nanotechnology Magazine 16 (2), 14-23, Apr. 2022.
  12. X. Y. Wang, Z. R. Wu, P. F. Zhou, H.H.C. Iu, S. M. Kang, J. K. Eshraghian, “FPGA synthesis of ternary memristor-CMOS decoders for active matrix microdisplays”, IEEE Transactions on Circuits and Systems I: Regular Papers, Mar. 2022
  13. P. H. Chen, C. Y. Lin, T. C. Chang, J. K. Eshraghian, Y. T. Chao, W. D. Lu, S.M. Sze, “Investigating Selectorless Property within Niobium Devices for Storage Applications”, ACS Applied Materials & Interfaces 14 (1), 2343-2350, 2022.
  14. Y. H. Kim, J. K. Eshraghian, Y. S. Goo, K. Cho, “Nanoscale Memristor‐based STDP Learning in a Radix‐X Quantized Retinal Neural Network”, Physica Status Solidi (a) 2022.
  15. P. Li, X. Wang, X. Zhang, J. K. Eshraghian, H. H. C. Iu, “Spice modelling of a tri‐state memristor and analysis of its series and parallel characteristics”, IET Circuits, Devices & Systems 16 (1), 81-91, 2022.

2021

  1. A. Jobin, J. K. Eshraghian, et al., “AI Reflections in 2021”, Nature Machine Intelligence, vol. 3, no. 1, Jan. 2021. [pdf]​
  2. S. Kang*, D. Choi, J. K. Eshraghian*, P. Zhou, J. Kim, B. S. Kong*, X. Zhu, A. S. Demirkol, A. Ascoli, R. Tetzlaff, W. D. Lu, L. O. Chua, “How to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation”, IEEE Trans. Circuits and Systems I: Regular Papers, Dec. 2021. (*Co-Corresponding Authors). [pdf] IEEE Darlington Best Paper Award.
  3. Q. Lin, J. Feng, J. Yuan, L. Liu, J. K. Eshraghian, H. Tong, M. Xu, X. Wang and X. Miao, “10 MA cm-2 current density in nanoscale conductive bridge threshold switching selector via densely localized cation sources”, Journal of Materials Chemistry C, vol. 9, Sept. 2021. [pdf]​
  4. S. Baek, G. H. Yu, J. Kim, C. T. Ngo, J. K. Eshraghian, J. P. Hong, “A Reconfigurable SRAM based CMOS PUF with Challenge to Response Pairs”, IEEE Access, vol. 9, May 2021. [pdf]
  5. P. Li, X. Wang, X. Zhang, J. K. Eshraghian, H. Iu, “Spice modelling of a tri-state memristor and analysis of its series and parallel characteristics”, IET Circuits, Devices & Systems, May 2021, p. e1-11.
  6. C. Lammie, J. K. Eshraghian, W. D. Lu and M. Azghadi, “Memristive Stochastic Computing for Deep Learning Parameter Optimization”, IEEE Trans. Circuits and Systems I: Express Briefs, vol. 68, no. 5, pp. 1650-1654, Mar. 2021 . [pdf]
  7. J. Lee, J. K. Eshraghian, S. Kim, K. Eshraghian, K. Cho, “Quantized Convolutional Neural Network Implementation on a Parallel-Connected Memristor Crossbar Array for Edge AI Platforms”, Journal of Nanoscience and Nanotechnology, vol. 21, no. 3, pp. 1854-1861, Mar. 2021.
  8. J. K. Eshraghian, J. Lee, S. Kim, K. Eshraghian and K. Cho, “A Leaky-Integrate-and-Fire Neuron Model of Spontaneous Reset of Thin-Film Metal-Oxide Resistive Switches”, Journal of Nanoscience and Nanotechnology, vol. 21, no. 3, pp. 1920-1926, Mar. 2021.
  9. S. You, J. K. Eshraghian, H. Iu and K. Cho, “Low-Power Wireless Sensor Network Using Fine-Grain Control of Sensor Module Power Mode”, Sensors, vol. 21, no. 9, p. e3198, Jan. 2021. [open-access]
  10. X. Wang, P. Zhou, J. K. Eshraghian, C. Lin, H. Iu, T. Chang and S. Kang, “High Density Memristor-CMOS Ternary Logic Family” IEEE Trans. Circuits and Systems I: Reg. Papers, vol. 68, no. 1, pp. 264-274, Jan. 2021. [pdf]
  11. Y. Yang*, N. D. Truong*, J. K. Eshraghian*, A. Nikpour, O. Kavehei, Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting, medRxiv, Nov. 2021. (*co-first author). [pdf]

2020

  1. C. Lin, Y. Tseng, P. Chen. T. Chang, J. K. Eshraghian, Q. Wang, Q. Lin, Y. Tan, M. Tai, W. Hung, H. Huang, W. D. Lu and S. Sze, “A High Speed MIM Resistive Memory Cell with an Inherent Vanadium Selector”, Applied Materials Today, vol. 21, Dec. 2020. [pdf]
  2. M. Azghadi, C. Lammie, J. K. Eshraghian, M. Payvand, E. Donati, B. Linares-Barranco and G. Indiveri, “Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications”, IEEE Transactions on Biomedical Circuits and System, vol. 14, no. 6, Dec. 2020, pp. 1138-1159. [pdf]
  3. C. Lin, J. Chen, P. Chen, T. Chang, Y. Wu, J. K. Eshraghian, J. Moon, Y. Wang, Z. Wang, H. Huang, Y. Li, X. Miao, W. D. Lu and S. Sze, “Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices”, Small, p. e2003964, Sep. 2020. [pdf]
  4. J. K. Eshraghian, Q. Lin, X. Wang, H.H.C. Iu, Q. Hu, H. Tong, “A Behavioral Model of Digital Resistive Switching for Systems Level DNN Acceleration”, IEEE Tran. Circuits and Syst. II: Express Briefs, vol. 67, no. 5, May. 2020, pp. 956-960. [pdf]
  5. J.K. Eshraghian, “Human Ownership of Artificial Creativity”, Nature Machine Intelligence, vol. 2, no. 3, March 2020, pp. 157-160.  [Springer] [pdf]
  6. M. Rahimi-Azghadi, Y.C. Cheng, J.K. Eshraghian, J. Chen, C.Y. Lin, A. Amirsoleimani, A. Mehonic, A.J. Kenyon, B. Fowler, J.C. Lee and Y.F. Chang, “CMOS and Memristive Hardware for Neuromorphic Computing”, Advanced Intelligence Systems, Mar. 2020, p. e1900189. [open-access]
  7. J. K. Eshraghian, S. Baek, T. Levi, T. Kohno, S. Al-Sarawi, P. Leong, K. Cho, D. Abbott and O. Kavehei, “Nonlinear Retinal Response Modeling for Future Neuromorphic Instrumentation”, IEEE Instrumentation & Measurement Magazine, February 2020, pp. 21-29. [IEEE Xplore]

2019

  1. C. Zheng, D. Yu, H.H.C. Iu, T. Fernando, T. Sun, J.K. Eshraghian, and H. Guo, “A Novel Universal Interface for Constructing Memory Elements for Circuit Applications”, IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 12, pp. 4793-4806, September 2019. [IEEE Xplore]
  2. J. Lee, J.K. Eshraghian, M. Jeong, F. Shan, H.H.C. Iu and K.R. Cho, “Nano-Programmable Logics Based on Double-Layer Anti-Facing Memristors”, Journal of Nanoscience and Nanotechnology, vol. 19, no. 3 pp. 1295-1300(6), March 2019.

2018

  1. J. K. Eshraghian, K. R. Cho, C. Zheng, M. Nam, H.H.C. Iu, W. Lei and K. Eshraghian, “Neuromorphic Vision Hybrid RRAM-CMOS Architecture”, IEEE Trans. Very-Large-Scale-Integration (VLSI) Systems, vol. 26, no. 12, December 2018, pp. 2816-2829. Best Paper Award. [pdf]
  2. J. K. Eshraghian, S. Baek, J.H. Kim, N. Iannella, K. Cho, Y.S. Goo, S.M. Kang and K. Eshraghian, “Formulation and Implementation of Nonlinear Integral Equations to Model Neural Dynamics Within the Vertebrate Retina”, International Journal of Neural Systems, vol. 28, no. 7, July 2018, p. e1850004. [ncbi]
  3. C. Zheng, H.H.C. Iu, T. Fernando, D.S. Yu, H. Guo and J.K. Eshraghian, “Analysis and Generation of Chaos Using Compositely Connected Coupled Memristors”, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 28, no. 6, Jun. 2018, p. e063115. [pdf]

2017

  1. J. K. Eshraghian, K.R. Cho, H.H.C. Iu, S.M. Kang and K. Eshraghian, “Maximization of Crossbar Array Memory Using Fundamental Memristor Theory”, IEEE Trans. Circuits and Syst. II: Express Briefs, vol. 64, no. 12, Dec. 2017, pp. 1402-1406. [IEEE Xplore]

2016

  1. S.W. Cho, J.K. Eshraghian, J.S. Eom, S.J. Kim and K.R. Cho, “Storage Logic Primitives Based on Stacked Memristor-CMOS Technology”, Journal of Nanoscience and Nanotechnology, vol. 16, no. 12, pp. 12726-12731(6), Dec. 2016.
  2. K.R. Cho, S. Baek, S. Cho, J. Kim, Y.S. Goo, J.K. Eshraghian, N. Iannella and K. Eshraghian, “Signal Flow Platform for Mapping and Simulation of Vertebrate Retina for Sensor Systems”, IEEE Sensors Journal, vol. 16, no. 15, pp. 5856-5866, Aug. 2016.

Conference Proceedings

  1. A. Kembay, K. Aguilar, J. K. Eshraghian, “A quantitative analysis of catastrophic forgetting in quantized spiking neural networks”, 2025 IEEE International Symposium on Circuits and Systems (ISCAS), May 2025.
  2. B. Nguyen, E. Mighetto, D. Louie, C. Yu, J. K. Eshraghian, “Closed-Loop Neuromorphic Deep Brain Stimulation using Deep Spiking Q-Networks”, 2025 IEEE International Symposium on Circuits and Systems (ISCAS), May 2025.
  3. S. Abreu, J. K. Eshraghian, “Algorithm-Hardware Co-Design for Ultra-Low-Power Large Language Models”, 2025 IEEE International Symposium on Circuits and Systems (ISCAS), May 2025.
  4. Y. Shan, M. Zhang, R. J. Zhu, X. Qiu, J. K. Eshraghian, H. Qu, “Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning,” AAAI 2025, April 2025.
  5. S. Abreu, S. B. Shrestha, R. J. Zhu, J. K. Eshraghian, “Neuromorphic principles for efficient large language models on Intel Loihi 2”, ICLR 2025 Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models, March 2025.
  6. R. Lin, R. J. Zhu, J. K. Eshraghian, “Reducing Data Bottlenecks in Distributed, Heterogeneous Neural Networks”, 17th IEEE International Symposium on Embedded Multicore/Manycore Systems-on-Chip, December 2024. Best Paper Award.
  7. R. J. Zhu, Z. Wang, L. Gilpin, J. K. Eshraghian, “Autonomous Driving with Spiking Neural Networks”, Neural Information Processing Systems (NeurIPS 2024), December 2024.
  8. J. K. Eshraghian, R. J. Zhu, “What do Transformers have to learn from Biological Spiking Neural Networks?”, 2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), September 2024.
  9. S. Gunasekaran, R. Zhu, Z. Kuncic, J. K. Eshraghian, “Knowledge Distillation Through Time for Future Event Prediction”, International Conference on Learning Representations (ICLR: Tiny Papers Track), Vienna, Austria, May 2024.
  10. S. Schmidgall, A. Krieger, J. K. Eshraghian, “Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots”, 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, May 2024. [pdf]
  11. B. Walters, Z. Cai, H. R. Kalatehbali, A. Amirsoleimani, R. Genov, J. Eshraghian, M. R. Azghadi, “Spiking Auto-Encoder Using Error Modulated Spike Timing Dependent Plasticity”, 2024 IEEE International Symposium on Circuits and Systems, Singapore, May 2024.
  12. S. Venkatesh, R. Marinescu, J. K. Eshraghian, “SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks”, Neuro-Inspired Computational Elements Conference (NICE 2024), San Diego, USA, April 2024.
  13. A. Samil Demirkol, A. Ascoli, J. K. Eshraghian, S. Kang, R. Tetzlaff, “A Qualitative Approach for the Design of a Locally Active Memristor Based Neuron Circuit”, IEEE 30th International Conference on Electronics, Circuits and Systems (ICECS), Istanbul, Turkey, December 2023.
  14. R. Zhu, J. K. Eshraghian, Z. Kuncic, “Memristive Reservoirs Learn to Learn”, International Conference on Neuromorphic Systems, Santa Fe, New Mexico, USA, August 2023.
  15. F. Modaresi, M. Guthaus, J. K. Eshraghian, “OpenSpike: An OpenRAM SNN Accelerator”, 2023 IEEE International Symposium on Circuits and Systems, Monterrey, California, USA, May 2023.
  16. P. Zhou, D. Choi, S. M. Kang, J. K. Eshraghian, “Backpropagating Errors Through Memristive Spiking Neural Networks”, 2023 IEEE International Symposium on Circuits and Systems, Monterrey, California, USA, May 2023.
  17. Y. Chen, S. Yu, J. K. Eshraghian, C. P. Lim, “Multi-Objective Neural Network for Optimal Wind Power Prediction Interval”, 2023 IEEE International Symposium on Circuits and Systems, Monterrey, California, USA, May 2023.
  18. T. Zhang, A. Amirsoleimani, J. K. Eshraghian, M. Rahimi-Azghadi, R. Genov, B. Xia, “SSCAE: A Neuromorphic SNN Autoencoder for sc-RNA-seq Dimensionality Reduction”, 2023 IEEE International Symposium on Circuits and Systems, Monterrey, California, USA, May 2023.
  19. Z. Zhang, D. Vigolo, J. K. Eshraghian, K. T. Yong, O. Kavehei, “Neuromorphic Cytometry: A novel attempt in image-based cell sorting”, Proceedings of Neuromorphic Materials, Devices, Circuits and Systems, Jan. 2023.
  20. J. K. Eshraghian, C. Lammie, M. R. Azghadi, W. D. Lu, “Navigating Local Minima in Quantized Spiking Neural Networks”, 2022 IEEE Artificial Intelligence Circuits and Systems Conference, Incheon, Korea, June 2022
  21. C. Lammie, J. K. Eshraghian, C. Li, A. Amirsoleimani, R. Genov, W. D. Lu, M. R. Azghadi, “Design space exploration of dense and sparse mapping schemes for RRAM architectures”, 2022 IEEE International Symposium on Circuits and Systems, Austin, Texas, USA, May 2022.
  22. P. Zhou, D. Choi, J. K. Eshraghian, S. M. Kang, “A Fully Memristive Spiking Neural Network with Unsupervised Learning”, 2022 IEEE International Symposium on Circuits and Systems, Austin, Texas, USA, May 2022.
  23. Y. C. Chen, J. K. Eshraghian, I. Shipley, M. Weiss, “Analog synaptic behaviors in carbon-based self-selective RRAM for in-memory supervised learning”, 2021 IEEE 71st Electronic Components and Technology Conference (ECTC), 1645-1651, 2021.
  24. C. Arrow, H. Wu, H. Iu, K. Nazarpour, J. K. Eshraghian, “Prosthesis Control Using Spike Rate Coding in the Retina Photoreceptor Cells”, 2021 IEEE International Symposium on Circuits and Systems, Daegu, Korea, May 2021.
  25. J. K. Eshraghian, K. Cho, S. Kang, “A 3-D Reconfigurable RRAM Crossbar Inference Engine”, 2021 IEEE International Symposium on Circuits and Systems, Daegu, Korea, May 2021.
  26. D. Robey, W. Thio, H. Iu, J. K. Eshraghian, “Naturalizing Neuromorphic Vision Event Streams Using Generative Adversarial Networks”, 2021 IEEE International Symposium on Circuits and Systems, Daegu, Korea, May 2021.
  27. J.K. Eshraghian, S.M. Kang, S. Baek, G. Orchard, H.H.C. Iu and W. Lei, “Analog Weights in ReRAM DNN Accelerators”, 2019 IEEE Artificial Intelligence Circuits and Systems Conference, March 2019. Best Paper Award.
  28. J.K. Eshraghian, C. Lammie, M.R. Azghadi, “Biologically Plausible Contrast Detection Using a Memristor Array”, 2020 IEEE International Symposium on Circuits and Systems, Seville, Spain, May 2020.
  29. J.K. Eshraghian, Q. Lin, H.H.C. Iu and X. Wang, “A Behavioral Model of Digital Resistive Switching for Systems Level DNN Acceleration”, 2020 IEEE International Symposium on Circuits and Systems, Seville, Spain, May 2020.
  30. A. Henson, T. Fiori, A. Auleear, I. McIntyre and J.K. Eshraghian, “A Transcranial Alternating Current Stimulator for Neural Entrainment”, 2020 IEEE International Symposium on Circuits and Systems, Seville, Spain, May 2020.
  31. J.K. Eshraghian, S. Baek, W. Thio, Y. Sandamirskaya, H.H.C. Iu and W. Lu, “A Real-Time Retinomorphic Simulator Using a Conductance-Based Discrete Neuronal Network”, 2020 IEEE Artificial Intelligence Circuits and Systems Conference, March 2020.
  32. S. Baek, J.K. Eshraghian, S.H. Ahn, A. James, K. Cho, “A Memristor-CMOS Braun Multiplier Array for Arithmetic Pipelining”, IEEE International Conference on Electronic Circuits and Systems, pp. 735-738, Nov. 2019.
  33. C. Zheng, J.K. Eshraghian, A. James and H.H.C. Iu, “Chaotic Oscillator Using Coupled Memristive Pairs”, IEEE International Conference on Electronic Circuits and Systems, pp. 462-465, Nov. 2019.
  34. J.K. Eshraghian, K.R. Cho, S.B. Baek, J.H. Kim, and K. Eshraghian, “Biological Modeling of Vertebrate Retina: Rod Cell to Bipolar Cell”, IEEE International Conference on Telecommunications and Signal Processing, Barcelona, Spain, Jul. 2017, pp. 391-394.
  35. J.K. Eshraghian, S.B. Baek, K.R. Cho, N. Iannella, J.H. Kim, Y.S. Goo, H.H.C. Iu, T. Fernando, and K. Eshraghian, “Modelling and analysis of signal flow platform implementation into retinal cell pathway”, IEEE Asia Pacific Conference on Circuits and Systems, Jeju, Republic of Korea, Oct. 2016.
  36. J.K. Eshraghian, H.H.C. Iu, T. Fernando, D. Yu, and Z. Li, “Modelling and characterization of dynamic behavior of coupled memristor circuits,” IEEE International Symposium on Circuits and Systems, Montreal, Canada, May 2016.

Live Demonstrations

C. Arrow, J. K. Eshraghian, H. Wu, H. H. C. Iu and K. Nazarpour, “Live Demonstration: Prosthetic Control Using a Real-Time Retina Cell Network Simulator”, 2020 IEEE International Conference on Electronics, Circuits & Systems, Glasgow, Scotland (Best Live Demo Award).

J.K. Eshraghian, S. Baek, W. Thio, Y. Sandamirskaya, H.H.C. Iu and W. Lu, “Live Demonstration: Video-to-Spike Conversion Using a Real-Time Retina Cell Network Simulator”, 2020 IEEE Artificial Intelligence Circuits and Systems Conference, Milan, Italy, March 2020, p. 131.

S.B. Baek, J.K. Eshraghian, K.R. Cho, N. Iannella, J.H. Kim, H.H.C. Iu, T. Fernando, K. Eshraghian, “Live demonstration: Signal flow platform implementation into retinal cell pathway”, IEEE Asia Pacific Conference on Circuits and Systems, Jeju, Republic of Korea, pp. 740-741, Oct. 2016

Book Chapters

B. Walters, C. Lammie, J. K. Eshraghian, C. Yakopcic, R. Genov, M. V. Jacob, A. Amirsoleimani, M. R. Azghadi, “Memristive Devices for Neuromorphic and Deep Learning Applications”, in Advanced Memory Technology, Y. Zhou (Editor), Royal Society of Chemistry, UK, 2023, pp. 680 – 704, ISBN: 978-1-83916-569-6.

J.K. Eshraghian, S.M. Kang and K. Eshraghian, “An Overview of Memristors in Neuromorphic Computing”, in Neuromorphic Circuits for Nanoscale Devices, P. Mazumder (Editor), River Publishers, The Netherlands, 2019, pp. 1-38, ISBN: 978-87-7022-060-6.

J.K. Eshraghian, H.H.C. Iu, and K. Eshraghian, “Modeling of Coupled Memristive-Based Architecture Applicable to Neural Networks”, in Memristor and Memristive Neural Networks, A. James (Editor), InTech Open Science, Austria, 2018, pp. 167-185, ISBN: 978-953-51-5481-5.

D.S. Yu, H.H.C. Iu, T. Fernando, and J.K. Eshraghian, “Memristive and Memcapactive Astable Multivibrators”, in Oscillator Circuits: Frontiers in Design, Analysis and Applications, Y. Nishio (Editor), IET, UK, 2017, pp. 51-68, ISBN: 978-1-78561-057-8.