Neural networks: a systematic introduction
Neural networks: a systematic introduction
A novel approach for the implementation of large scale spiking neural networks on FPGA hardware
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
IEEE Transactions on Neural Networks
A CMOS analog adaptive BAM with on-chip learning and weight refreshing
IEEE Transactions on Neural Networks
Practical approach to programmable analog circuits with memristors
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Non linear dynamics of memristor based 3rd order oscillatory system
Microelectronics Journal
Hardware realization of BSB recall function using memristor crossbar arrays
Proceedings of the 49th Annual Design Automation Conference
Analysis of current–voltage characteristics for memristive elements in pattern recognition systems
International Journal of Circuit Theory and Applications
An expanded HP memristor model for memristive neural network
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Memristor-based memory: The sneak paths problem and solutions
Microelectronics Journal
Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses
Neural Processing Letters
2013 Special Issue: Adaptive Neuromorphic Architecture (ANA)
Neural Networks
Comparison of two memristor based neural network learning schemes for crossbar architecture
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be ''plastic'' according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behavior with electronic neural networks.