Memristor-based neural networks

@article{Thomas2013MemristorbasedNN,
  title={Memristor-based neural networks},
  author={Andy Thomas},
  journal={Journal of Physics D},
  year={2013},
  volume={46},
  pages={093001}
}
  • Andy Thomas
  • Published 2013
  • Computer Science
  • Journal of Physics D
The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in… Expand
Memristive-Based Neuromorphic Applications and Associative Memories
TLDR
The neuromorphic properties of memristors are reviewed, comparing them with the key players of neuronal computations, synapses and neurons, and emphasis is given to memristive-based associative memories, a bio-inspired content addressable memory system which relevant properties such as distributed storage and noise correction. Expand
A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems
TLDR
A synapse structure is presented that utilizes a pair of memristors, to implement both positive and negative weights, and is shown to have high accuracy when used in neural networks for classification tasks. Expand
Behavioral Modeling and STDP Learning Characteristics of a Memristive Synapse
TLDR
Results confirm that the proposed memristor model is an attractive candidate for complex spiking neural networks. Expand
Stochastic Memristive Interface for Neural Signal Processing
TLDR
The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications. Expand
A Hardware Friendly Unsupervised Memristive Neural Network with Weight Sharing Mechanism
TLDR
A hardware friendly MNN circuit is introduced, in which the memristive characteristics are implemented by digital integrated circuit, and through this method, spike timing dependent plasticity (STDP) and unsupervised learning are realized. Expand
Programming of Memristive Artificial Synaptic Crossbar Network Using PWM Techniques
TLDR
A systematic investigation was conducted on memristor-bacteria, which resembles an artificial synapse and is considered to be basic electronic element for realizing neuromorphic circuits. Expand
Memristor Memristive System Hysteresis Model System Model Physical Model Flux-Controlled Multilevel DBMD
Neuromorphic circuits mimic partial functionalities of brain in a bio-inspired information processing sense in order to achieve similar efficiencies as biological systems. While there are commonExpand
A Compact Memristor-Based Dynamic Synapse for Spiking Neural Networks
TLDR
A Memristor-based dynamic (MD) synapse design with experiment-calibrated memristor models is proposed and a temporal pattern learning application was investigated to evaluate the use of MD synapses in spiking neural networks, under both spike-timing-dependent plasticity and remote supervised method learning rules. Expand
Generic Wave Digital Emulation of Memristive Devices
TLDR
A generic memristive emulator based on wave digital principles is introduced, which is flexible, robust, efficient, and it preserves the passivity of the real device, which makes it reusable independent of a particular application. Expand
Stochastic Memristive Interface between Electronic FitzHugh-Nagumo Neurons
The dynamics of memristive device in response to neuron-like signals and coupling electronic neurons via memristive device has been investigated theoretically and experimentally. The simplestExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 177 REFERENCES
Experimental demonstration of associative memory with memristive neural networks
TLDR
This work has demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses and opens up new possibilities in the understanding of neural processes using memory devices. Expand
Hebbian Learning in Spiking Neural Networks With Nanocrystalline Silicon TFTs and Memristive Synapses
Characteristics similar to biological neurons are demonstrated in SPICE simulations of spiking neuron circuits comprised of submicron nanocrystalline silicon (nc-Si) thin-film transistors (TFTs).Expand
Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors
TLDR
Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology, and behavior indicates that the system can learn to detect which signals are important in the general population and that there is a spike-timing-dependent component of the learning mechanism. Expand
The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system.
TLDR
This work used memristive magnetic tunnel junctions based on MgO to demonstrate that the synaptic functionality is complemented by neuron-like behavior in these nanoscopic devices, and showed that a phenomenon known as back-hopping leads to repeated switching between two resistance levels accompanied by current spiking, which emulates neuronal behavior. Expand
MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS
TLDR
By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of Memristor cellular automaton and can perform more than one function at the same time, that is, it allows multitasking. Expand
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
TLDR
A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed, and both positive and negative multiplications are performed via a charge-dependent Ohm's law. Expand
STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks
Implementation of a correlation-based learning rule, Spike-Timing-Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using `memristive' nanodevice. STDP is performedExpand
Neuromorphic architectures for nanoelectronic circuits
TLDR
It is shown that despite the hardware-imposed limitations, a simple weight import procedure allows the CrossNets using simple two-terminal nanodevices to perform functions that had been earlier demonstrated in neural networks with continuous, determin- istic synaptic weights. Expand
Nanoscale memristor device as synapse in neuromorphic systems.
TLDR
A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Expand
Memristive model of amoeba learning.
TLDR
It is shown that the amoebalike cell Physarum polycephalum when exposed to a pattern of periodic environmental changes learns and adapts its behavior in anticipation of the next stimulus to come and is useful to better understand the origins of primitive intelligence. Expand
...
1
2
3
4
5
...