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A memristive network is a type of artificial neural network that is based on memristive devices, which are electronic components that exhibit the property of memristance. In a memristive network, the memristive devices are used to simulate the behavior of neurons and synapses in the human brain. The network consists of layers of memristive ...
A silver nanowire connectome [10] can be described using graph theory, and have applications ranging from sensors to information storage.Since memristive devices behave as axons in a neuronal network, the theory of memristive networks is the theory of nanoscale electric physical devices whose behavior parallels the one of real neuronal circuits.
While the memristor is defined in terms of a two-terminal circuit element, there was an implementation of a three-terminal device called a memistor developed by Bernard Widrow in 1960. Memistors formed basic components of a neural network architecture called ADALINE developed by Widrow. [1] [2] The memistor was also used in MADALINE.
Molecular-beam epitaxy (MBE) is an epitaxy method for thin-film deposition of single crystals. MBE is widely used in the manufacture of semiconductor devices, including transistors. [1] MBE is used to make diodes and MOSFETs (MOS field-effect transistors) at microwave frequencies, and to manufacture the lasers used to read optical discs (such ...
For example, there is a prototype, photonic, quantum memristive device for neuromorphic (quantum-)computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications, [367] [368] and quantum machine learning is a field with some variety of applications under ...
NEC and Toshiba used this process for their 4 Mb DRAM memory chips in 1986. [47] Hitachi, IBM, Matsushita and Mitsubishi Electric used this process for their 4 Mb DRAM memory chips in 1987. [37] Toshiba's 4 Mb EPROM memory chip in 1987. [47] Hitachi, Mitsubishi and Toshiba used this process for their 1 Mb SRAM memory chips in 1987. [47]
The two most prevalent shape-memory alloys are copper-aluminium-nickel and nickel-titanium (), but SMAs can also be created by alloying zinc, copper, gold and iron.Although iron-based and copper-based SMAs, such as Fe-Mn-Si, Cu-Zn-Al and Cu-Al-Ni, are commercially available and cheaper than NiTi, NiTi-based SMAs are preferable for most applications due to their stability and practicability [1 ...
These types of devices would allow for a synapse model that could realise a learning rule, by which the synaptic efficacy is altered by voltages applied to the terminals of the device. An example of such a learning rule is spike-timing-dependant-plasticty by which the weight of the synapse, in this case the conductivity, could be modulated ...