A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model.[1] "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.[2][3]

Types of physical neural networks

ADALINE

In the 1960s Bernard Widrow and Ted Hoff developed ADALINE (Adaptive Linear Neuron) which used electrochemical cells called memistors (memory resistors) to emulate synapses of an artificial neuron.[4] The memistors were implemented as 3-terminal devices operating based on the reversible electroplating of copper such that the resistance between two of the terminals is controlled by the integral of the current applied via the third terminal. The ADALINE circuitry was briefly commercialized by the Memistor Corporation in the 1960s enabling some applications in pattern recognition. However, since the memistors were not fabricated using integrated circuit fabrication techniques the technology was not scalable and was eventually abandoned as solid-state electronics became mature.[5]

Analog VLSI

In 1989 Carver Mead published his book Analog VLSI and Neural Systems,[6] which spun off perhaps the most common variant of analog neural networks. The physical realization is implemented in analog VLSI. This is often implemented as field effect transistors in low inversion. Such devices can be modelled as translinear circuits. This is a technique described by Barrie Gilbert in several papers around mid 1970th, and in particular his Translinear Circuits from 1981.[7][8] With this method circuits can be analyzed as a set of well-defined functions in steady-state, and such circuits assembled into complex networks.

Physical Neural Network

Alex Nugent describes a physical neural network as one or more nonlinear neuron-like nodes used to sum signals and nanoconnections formed from nanoparticles, nanowires, or nanotubes which determine the signal strength input to the nodes.[9] Alignment or self-assembly of the nanoconnections is determined by the history of the applied electric field performing a function analogous to neural synapses. Numerous applications[10] for such physical neural networks are possible. For example, a temporal summation device [11] can be composed of one or more nanoconnections having an input and an output thereof, wherein an input signal provided to the input causes one or more of the nanoconnection to experience an increase in connection strength thereof over time. Another example of a physical neural network is taught by U.S. Patent No. 7,039,619[12] entitled "Utilized nanotechnology apparatus using a neural network, a solution and a connection gap," which issued to Alex Nugent by the U.S. Patent & Trademark Office on May 2, 2006.[13]

A further application of physical neural network is shown in U.S. Patent No. 7,412,428 entitled "Application of hebbian and anti-hebbian learning to nanotechnology-based physical neural networks," which issued on August 12, 2008.[14]

Nugent and Molter have shown that universal computing and general-purpose machine learning are possible from operations available through simple memristive circuits operating the AHaH plasticity rule.[15] More recently, it has been argued that also complex networks of purely memristive circuits can serve as neural networks. [16] [17]

Phase change neural network

In 2002, Stanford Ovshinsky described an analog neural computing medium in which phase-change material has the ability to cumulatively respond to multiple input signals.[18] An electrical alteration of the resistance of the phase change material is used to control the weighting of the input signals.

Memristive neural network

Greg Snider of HP Labs describes a system of cortical computing with memristive nanodevices.[19] The memristors (memory resistors) are implemented by thin film materials in which the resistance is electrically tuned via the transport of ions or oxygen vacancies within the film. DARPA's SyNAPSE project has funded IBM Research and HP Labs, in collaboration with the Boston University Department of Cognitive and Neural Systems (CNS), to develop neuromorphic architectures which may be based on memristive systems.[20]

Protonic artificial synapses

In 2022, researchers reported the development of nanoscale brain-inspired artificial synapses, using the ion proton (H+
), for 'analog deep learning'.[21][22]

See also

References

  1. ↑ Lawrence, Celestine P. (2022), "Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron", IEEE Transactions on Electron Devices, 69 (9): 5373–5376, doi:10.1109/TED.2022.3191956, S2CID 251340897
  2. ↑ "Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training | Synced". 27 May 2021.
  3. ↑ "Nano-spaghetti to solve neural network power consumption".
  4. ↑ Widrow, B.; Pierce, W. H.; Angell, J.B. (1961), "Birth, Life, and Death in Microelectronic Systems" (PDF), Technical Report No. 1552-2/1851-1
  5. ↑ Anderson, James; Rosenfeld, Edward (1998), Talking Nets: An Oral History of Neural Networks, MIT Press, ISBN 978-0-262-01167-9
  6. ↑ Mead, Carver. (1989). Analog VLSI and neural systems. Reading, Mass.: Addison-Wesley. ISBN 0-201-05992-4. OCLC 17954003.
  7. ↑ Gilbert, Barrie (1981), Translinear Circuits (Handout, pp. 81)
  8. ↑ Gilbert, Barrie (1999-12-27), "Translinear Circuits", Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, Inc., doi:10.1002/047134608x.w2302, ISBN 0-471-34608-X
  9. ↑ U.S. Patent 6,889,216
  10. ↑ U.S. Known Patents
  11. ↑ U.S. Patent No. 7,028,017
  12. ↑ "Utilized nanotechnology apparatus using a neutral network, a solution and a connection gap".
  13. ↑ "United States Patent: 8918353 - Methods and systems for feature extraction".
  14. ↑ "United States Patent: 9104975 - Memristor apparatus".
  15. ↑ Nugent, Michael Alexander; Molter, Timothy Wesley (2014). "AHaH Computing–From Metastable Switches to Attractors to Machine Learning". PLOS ONE. 9 (2): e85175. Bibcode:2014PLoSO...985175N. doi:10.1371/journal.pone.0085175. PMC 3919716. PMID 24520315.
  16. ↑ Caravelli, F.; Traversa, F. L.; Di Ventra, M. (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.
  17. ↑ Caravelli, F. (2019). "Asymptotic behavior of memristive circuits". Entropy. 21 (8): 789. arXiv:1712.07046. Bibcode:2019Entrp..21..789C. doi:10.3390/e21080789. PMC 7515318. PMID 33267502.
  18. ↑ U.S. Patent 6,999,953
  19. ↑ Snider, Greg (2008), "Cortical computing with memristive nanodevices", Sci-DAC Review, 10: 58–65, archived from the original on 2016-05-16, retrieved 2009-10-26
  20. ↑ Caravelli, Francesco; Carbajal, Juan Pablo (2018), "Memristors for the curious outsiders", Technologies, 6 (4): 118, arXiv:1812.03389, Bibcode:2018arXiv181203389C, doi:10.3390/technologies6040118, S2CID 54464654
  21. ↑ "'Artificial synapse' could make neural networks work more like brains". New Scientist. Retrieved 21 August 2022.
  22. ↑ Onen, Murat; Emond, Nicolas; Wang, Baoming; Zhang, Difei; Ross, Frances M.; Li, Ju; Yildiz, Bilge; del Alamo, Jesús A. (29 July 2022). "Nanosecond protonic programmable resistors for analog deep learning" (PDF). Science. 377 (6605): 539–543. doi:10.1126/science.abp8064. ISSN 0036-8075. PMID 35901152. S2CID 251159631.
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