A physical neural network is a type of artificial neural network in which an electrically adjustable resistance material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches which simulate neural networks. This terminology has been used to describe a type of artificial neural network patented by inventor Alex Nugent of KnowmTech in which neural synapses are based on variable resistance nanoconnections [1]. 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.
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In the 1960's Bernard Widrow and Ted Hoff developed ADALINE (Adaptive Linear Neuron) which used electrochemical cells called memistors (memory transistors) to emulate synapses of an artificial neuron[2]. 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 1960’s 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[3].
Alex Nugent describes a "Knowm" as a physical neural network including 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 [1]. 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[4] for such "Knowm" physical neural networks are possible. For example, a temporal summation device [5]
can 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.
Stanford Ovshinsky describes an analog neural computing medium in which phase change material has the ability to cumulatively respond to multiple input signals [6]. An electrical alteration of the resistance of the phase change material is used to control the weighting of the input signals.
Greg Snider of HP Labs describes a system of cortical computing with memristive nanodevices.[7] 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.