Physical neural network

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.{{citation | url-access = registration

Analog VLSI

In 1989 Carver Mead published his book Analog VLSI and Neural Systems, 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. 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. 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 for such physical neural networks are possible. For example, a temporal summation device 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 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.

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.

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. More recently, it has been argued that also complex networks of purely memristive circuits can serve as neural networks.

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. 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.{{Citation | access-date = 2009-10-26 | archive-url = https://web.archive.org/web/20160516070906/http://www.scidacreview.org/0804/html/hardware.html | archive-date = 2016-05-16

Protonic artificial synapses

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

References

References

1. (27 May 2021). "Cornell & NTT's Physical Neural Networks: A "Radical Alternative for Implementing Deep Neural Networks" That Enables Arbitrary Physical Systems Training | Synced".
2. "Nano-spaghetti to solve neural network power consumption".
3. Mead, Carver.. (1989). "Analog VLSI and neural systems". Addison-Wesley.
4. Gilbert, Barrie. (1981). "Translinear Circuits".
5. {{Citation. Gilbert. Barrie. (1999-12-27). John Wiley & Sons, Inc.
6. {{US Patent. 6889216
7. [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=0&f=S&l=50&TERM1=alex&FIELD1=INNM&co1=AND&TERM2=nugent&FIELD2=INNM&d=PTXT U.S. Known Patents]
8. U.S. [[Patent]] No. [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=17&f=G&l=50&co1=AND&d=PTXT&s1=alex.INNM.&s2=nugent.INNM.&OS=IN/alex+AND+IN/nugent&RS=IN/alex+AND+IN/nugent 7,028,017]
9. "Utilized nanotechnology apparatus using a neutral network, a solution and a connection gap".
10. "United States Patent: 8918353 - Methods and systems for feature extraction".
11. "United States Patent: 9104975 - Memristor apparatus".
12. (2014). "AHaH Computing–From Metastable Switches to Attractors to Machine Learning". PLOS ONE.
13. (2017). "The complex dynamics of memristive circuits: analytical results and universal slow relaxation". Physical Review E.
14. Caravelli, F.. (2019). "Asymptotic behavior of memristive circuits". Entropy.
15. {{US Patent. 6999953
16. "'Artificial synapse' could make neural networks work more like brains". New Scientist.
17. (29 July 2022). "Nanosecond protonic programmable resistors for analog deep learning". Science.