Back هندسة التشكيل العصبي Arabic Невроморфно инженерство Bulgarian Enginyeria neuromòrfica Catalan Neuromorphic Engineering German Ingeniería neuromórfica Spanish نورومورفیک Persian Informatique neuromorphique French Ingegneria neuromorfica Italian ニューロモルフィック・エンジニアリング Japanese 뉴로모픽 엔지니어링 Korean

Neuromorphic engineering

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain.[1][2] A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations.[3][4] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.[5]

The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories, threshold switches, transistors,[7][4] among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch,[8] or using canonical learning rules from the biological learning literature, e.g., using BindsNet.[9]

A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering[4] to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[10] One of the first applications for neuromorphic engineering was proposed by Carver Mead[11] in the late 1980s.

  1. ^ Ham, Donhee; Park, Hongkun; Hwang, Sungwoo; Kim, Kinam (2021). "Neuromorphic electronics based on copying and pasting the brain". Nature Electronics. 4 (9): 635–644. doi:10.1038/s41928-021-00646-1. ISSN 2520-1131. S2CID 240580331.
  2. ^ van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto (April 2017). "A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing". Nature Materials. 16 (4): 414–418. Bibcode:2017NatMa..16..414V. doi:10.1038/nmat4856. ISSN 1476-4660. PMID 28218920.
  3. ^ Mead, Carver (1990). "Neuromorphic electronic systems" (PDF). Proceedings of the IEEE. 78 (10): 1629–1636. doi:10.1109/5.58356. S2CID 1169506.
  4. ^ a b c Rami A. Alzahrani; Alice C. Parker (July 2020). Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling. International Conference on Neuromorphic Systems 2020. pp. 1–8. doi:10.1145/3407197.3407204. S2CID 220794387.
  5. ^ Tomassoli, Laura; Silva-Dias, Leonardo; Dolnik, Milos; Epstein, Irving R.; Germani, Raimondo; Gentili, Pier Luigi (February 8, 2024). "Neuromorphic Engineering in Wetware: Discriminating Acoustic Frequencies through Their Effects on Chemical Waves". The Journal of Physical Chemistry B. 128 (5): 1241–1255. doi:10.1021/acs.jpcb.3c08429. ISSN 1520-6106.
  6. ^ Maan, A. K.; Jayadevi, D. A.; James, A. P. (January 1, 2016). "A Survey of Memristive Threshold Logic Circuits". IEEE Transactions on Neural Networks and Learning Systems. PP (99): 1734–1746. arXiv:1604.07121. Bibcode:2016arXiv160407121M. doi:10.1109/TNNLS.2016.2547842. ISSN 2162-237X. PMID 27164608. S2CID 1798273.
  7. ^ Zhou, You; Ramanathan, S. (August 1, 2015). "Mott Memory and Neuromorphic Devices". Proceedings of the IEEE. 103 (8): 1289–1310. doi:10.1109/JPROC.2015.2431914. ISSN 0018-9219. S2CID 11347598.
  8. ^ Eshraghian, Jason K.; Ward, Max; Neftci, Emre; Wang, Xinxin; Lenz, Gregor; Dwivedi, Girish; Bennamoun, Mohammed; Jeong, Doo Seok; Lu, Wei D. (October 1, 2021). "Training Spiking Neural Networks Using Lessons from Deep Learning". arXiv:2109.12894 [cs.NE].
  9. ^ "Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch". GitHub. March 31, 2020.
  10. ^ Boddhu, S. K.; Gallagher, J. C. (2012). "Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers". Applied Computational Intelligence and Soft Computing. 2012: 1–21. doi:10.1155/2012/705483.
  11. ^ Mead, Carver A.; Mahowald, M. A. (January 1, 1988). "A silicon model of early visual processing". Neural Networks. 1 (1): 91–97. doi:10.1016/0893-6080(88)90024-X. ISSN 0893-6080.
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