Delayed neuromorphic systems mimic biological neural networks by incorporating time delays, enabling complex dynamics like oscillations and high-dimensional behaviour for tasks. Time-delay reservoir computing, using minimal hardware, achieves efficient, low-power processing at high speeds. Challenges include architectural trade-offs, limited delay tunability, simulation complexity, and stability control. This note examines the potential and limitations of these systems for scalable, energy-efficient computing in edge devices and brain-machine interfaces.
Azizi, Y. & Mohseni, M. (2026). Short Note: Delayed Neuromorphic Systems. Pioneering Advances in Materials, 1(2), 1–3. https://doi.org/10.48308/piadm.2025.241311.1008
MLA
Azizi, Y., & Mohseni, M. "Short Note: Delayed Neuromorphic Systems", Pioneering Advances in Materials, 1, 2, 2026, 1–3. doi: 10.48308/piadm.2025.241311.1008
HARVARD
Azizi, Y., & Mohseni, M. (2026). 'Short Note: Delayed Neuromorphic Systems', Pioneering Advances in Materials, 1(2), pp. 1–3. doi: 10.48308/piadm.2025.241311.1008
CHICAGO
Azizi, Y. & Mohseni, M., "Short Note: Delayed Neuromorphic Systems." Pioneering Advances in Materials, 1 2 (2026): 1–3, doi: 10.48308/piadm.2025.241311.1008
VANCOUVER
Azizi Y., Mohseni M. Short Note: Delayed Neuromorphic Systems. Pioneering Advances in Materials. 2026;1(2):1–3. doi: 10.48308/piadm.2025.241311.1008
