Neuromorphic computing

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Neuromorphic computing
InventorCarver Mead

Neuromorphic computing is a computing approach inspired by the human brain's structure and function. [1] [2] It uses artificial neurons to perform computations, mimicking neural systems for tasks such as perception, motor control, and multisensory integration. [3] These systems, implemented in analog, digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing elements. [4] This interdisciplinary field integrates biology, physics, mathematics, computer science, and electronic engineering to develop systems that emulate the brain’s morphology and computational strategies. [5] Neuromorphic systems aim to enhance energy efficiency and computational power for applications including artificial intelligence, pattern recognition, and sensory processing.

Contents

History

Carver Mead proposed one of the first applications for neuromorphic engineering in the late 1980s. [6] In 2006, researchers at Georgia Tech developed a field programmable neural array, a silicon-based chip modeling neuron channel-ion characteristics. [7] In 2011, MIT researchers created a chip mimicking synaptic communication using 400 transistors and standard CMOS techniques. [8] [9]

In 2012 HP Labs researchers reported that Mott memristors exhibit volatile behavior at low temperatures, enabling the creation of neuristors that mimic neuron behavior and support Turing machine components. [10] Also in 2012, Purdue University researchers presented a neuromorphic chip design using lateral spin valves and memristors, noted for energy efficiency. [11]

The 2013 Blue Brain Project creates detailed digital models of rodent brains. [12]

Neurogrid, developed by Brains in Silicon at Stanford University, used 16 NeuroCore chips to emulate 65,536 neurons with high energy efficiency in 2014. [13] The 2014 BRAIN Initiative and IBM’s TrueNorth chip contributed to neuromorphic advancements. [14]

The 2016 BrainScaleS project, a hybrid neuromorphic supercomputer at University of Heidelberg, operated 864 times faster than biological neurons. [15]

In 2017, Intel unveiled its Loihi chip, using an asynchronous spiking neural network for efficient learning and inference. [16] Also in 2017 IMEC’s self-learning chip, based on OxRAM, demonstrated music composition by learning from minuets. [17]

In 2022, MIT researchers developed artificial synapses using protons for analog deep learning. [18] In 2019, the European Union funded neuromorphic quantum computing to explore quantum operations using neuromorphic systems. [19] Also in 2022, researchers at the Max Planck Institute for Polymer Research developed an organic artificial spiking neuron for in-situ neuromorphic sensing and biointerfacing. [20]

Researchers reported in 2024 that chemical systems in liquid solutions can detect sound at various wavelengths, offering potential for neuromorphic applications. [21]

Neurological inspiration

Neuromorphic engineering emulates the brain’s structure and operations, focusing on the analog nature of biological computation and the role of neurons in cognition. The brain processes information via neurons using chemical signals, abstracted into mathematical functions. Neuromorphic systems distribute computation across small elements, similar to neurons, using methods guided by anatomical and functional neural maps from electron microscopy and neural connection studies. [22] [23]

Implementation

Neuromorphic systems employ hardware such as oxide-based memristors, spintronic memories, threshold switches, and transistors. [24] [25] Software implementations train spiking neural networks using error backpropagation. [26] [27]

Neuromemristive systems

Neuromemristive systems use memristors to implement neuroplasticity, focusing on abstract neural network models rather than detailed biological mimicry. [28] These systems enable applications in speech recognition, face recognition, and object recognition, and can replace conventional digital logic gates. [29] The Caravelli-Traversa-Di Ventra equation describes memristive memory evolution, revealing tunneling phenomena and Lyapunov functions. [30]

Neuromorphic sensors

Neuromorphic principles extend to sensors, such as the retinomorphic sensor or event camera, which mimic human vision by registering brightness changes individually, optimizing power consumption. [31]

An example of this applied to detecting light is the retinomorphic sensor or, when employed in an array, an event camera.

Ethical considerations

Neuromorphic systems raise the same ethical questions as those for other approaches to artificial intelligence. Daniel Lim argued that advanced neuromorphic systems could lead to machine consciousness, raising concerns about whether civil rights and other protocols should be extended to them. [32] Legal debates, such as in Acohs Pty Ltd v. Ucorp Pty Ltd, question ownership of work produced by neuromorphic systems, as non-human-generated outputs may not be copyrightable. [33]

See also

References

  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. ↑ 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 .
  5. ↑ 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.
  6. ↑ Mead, Carver A.; Mahowald, M. A. (January 1, 1988). "A silicon model of early visual processing %2888%2990024-X". Neural Networks. 1 (1): 91–97. doi:10.1016/0893-6080(88)90024-X. ISSN   0893-6080.
  7. ↑ Farquhar, Ethan; Hasler, Paul. (May 2006). "A Field Programmable Neural Array". 2006 IEEE International Symposium on Circuits and Systems. pp. 4114–4117. doi:10.1109/ISCAS.2006.1693534. ISBN   978-0-7803-9389-9. S2CID   206966013.
  8. ↑ "MIT creates "brain chip"". November 15, 2011. Retrieved December 4, 2012.
  9. ↑ Poon, Chi-Sang; Zhou, Kuan (2011). "Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities". Frontiers in Neuroscience. 5: 108. doi: 10.3389/fnins.2011.00108 . PMC   3181466 . PMID   21991244.
  10. ↑ Pickett, M. D.; Medeiros-Ribeiro, G.; Williams, R. S. (2012). "A scalable neuristor built with Mott memristors". Nature Materials. 12 (2): 114–7. Bibcode:2013NatMa..12..114P. doi:10.1038/nmat3510. PMID   23241533. S2CID   16271627.
  11. ↑ Sharad, Mrigank; Augustine, Charles; Panagopoulos, Georgios; Roy, Kaushik (2012). "Proposal For Neuromorphic Hardware Using Spin Devices". arXiv: 1206.3227 [cond-mat.dis-nn].
  12. ↑ "Involved Organizations". Archived from the original on March 2, 2013. Retrieved February 22, 2013.
  13. ↑ Boahen, Kwabena (April 24, 2014). "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations". Proceedings of the IEEE. 102 (5): 699–716. doi:10.1109/JPROC.2014.2313565. S2CID   17176371.
  14. ↑ Modha, Dharmendra (August 2014). "A million spiking-neuron integrated circuit with a scalable communication network and interface". Science. 345 (6197): 668–673. Bibcode:2014Sci...345..668M. doi:10.1126/science.1254642. PMID   25104385. S2CID   12706847.
  15. ↑ "Beyond von Neumann, Neuromorphic Computing Steadily Advances". HPCwire. March 21, 2016. Retrieved October 8, 2021.
  16. ↑ Davies, Mike; et al. (January 16, 2018). "Loihi: A Neuromorphic Manycore Processor with On-Chip Learning". IEEE Micro. 38 (1): 82–99. Bibcode:2018IMicr..38a..82D. doi:10.1109/MM.2018.112130359. S2CID   3608458.
  17. ↑ Bourzac, Katherine (May 23, 2017). "A Neuromorphic Chip That Makes Music". IEEE Spectrum. Retrieved October 1, 2019.
  18. ↑ Onen, Murat; Emond, Nicolas; Wang, Baoming; Zhang, Difei; Ross, Frances M.; Li, Ju; Yildiz, Bilge; del Alamo, Jesús A. (July 29, 2022). "Nanosecond protonic programmable resistors for analog deep learning" (PDF). Science. 377 (6605): 539–543. Bibcode:2022Sci...377..539O. doi:10.1126/science.abp8064. ISSN   0036-8075. PMID   35901152. S2CID   251159631.
  19. ↑ "Neuromorphic Quantum Computing | Quromorphic Project | Fact Sheet | H2020" . CORDIS | European Commission. doi:10.3030/828826 . Retrieved March 18, 2024.
  20. ↑ Sarkar, Tanmoy; Lieberth, Katharina; Pavlou, Aristea; Frank, Thomas; Mailaender, Volker; McCulloch, Iain; Blom, Paul W. M.; Torriccelli, Fabrizio; Gkoupidenis, Paschalis (November 7, 2022). "An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing". Nature Electronics. 5 (11): 774–783. doi: 10.1038/s41928-022-00859-y . hdl: 10754/686016 . ISSN   2520-1131. S2CID   253413801.
  21. ↑ 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. PMID   38285636.
  22. ↑ Devineni, Anita (October 2, 2024). "A complete map of the fruit-fly". Nature. 634 (8032): 35–36. doi:10.1038/d41586-024-03029-6. PMID   39358530.
  23. ↑ Wang, Jun; Jung, Woo-Bin; Gertner, Rona; Park, Hongkun; Ham, Donhee (2025). "Synaptic connectivity mapping among thousands of neurons via parallelized intracellular recording with a microhole electrode array" . Nature Biomedical Engineering. 9 (7): 1144–1154. doi:10.1038/s41551-025-01352-5. PMID   39934437.
  24. ↑ 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.
  25. ↑ 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.
  26. ↑ 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].
  27. ↑ "Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch". GitHub . March 31, 2020.
  28. ↑ "002.08 N.I.C.E. Workshop 2014: Towards Intelligent Computing with Neuromemristive Circuits and Systems – Feb. 2014". digitalops.sandia.gov. Retrieved August 26, 2019.
  29. ↑ Maan, A.K.; James, A.P.; Dimitrijev, S. (2015). "Memristor pattern recogniser: isolated speech word recognition". Electronics Letters. 51 (17): 1370–1372. Bibcode:2015ElL....51.1370M. doi:10.1049/el.2015.1428. hdl: 10072/140989 . S2CID   61454815.
  30. ↑ Caravelli; et al. (2017). "The complex dynamics of memristive circuits: analytical results and universal slow relaxation". Physical Review E. 95 (2) 022140. arXiv: 1608.08651 . Bibcode:2017PhRvE..95b2140C. doi:10.1103/PhysRevE.95.022140. PMID   28297937. S2CID   6758362.
  31. ↑ Skorka, Orit (July 1, 2011). "Toward a digital camera to rival the human eye" . Journal of Electronic Imaging. 20 (3): 033009–033009–18. Bibcode:2011JEI....20c3009S. doi:10.1117/1.3611015. ISSN   1017-9909.
  32. ↑ Lim, Daniel (June 1, 2014). "Brain simulation and personhood: a concern with the Human Brain Project". Ethics and Information Technology. 16 (2): 77–89. doi:10.1007/s10676-013-9330-5. ISSN   1572-8439. S2CID   17415814.
  33. ↑ Lavan. "Copyright in source code and digital products". Lavan. Retrieved May 10, 2019.
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