Memcapacitive neural networks

Abstract

We show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural networks. As an example of our approach, we discuss the architecture of an integrate-and-fire neural network based on memcapacitive synapses. Moreover, we demonstrate that the spike-timing-dependent plasticity can be simply realized with some of these devices. Memcapacitive synapses are a low-energy alternative to memristive synapses for neuromorphic computation.

References in corpus (2)

• Memcomputing: a computing paradigm to store and process information on the same physical platform
• Reliable SPICE Simulations of Memristors, Memcapacitors and Meminductors

Cited by in corpus (6)

• Hardware design of LIF with Latency neuron model with memristive STDP synapses
• Memcomputing with membrane memcapacitive systems
• Biomembrane-based Memcapacitive Reservoir Computing System for Energy Efficient Temporal Data Processing
• An Adiabatic Capacitive Artificial Neuron with RRAM-based Threshold Detection for Energy-Efficient Neuromorphic Computing
• Electromechanical memcapacitive neurons for energy-efficient spiking neural networks
• Adiabatic Capacitive Neuron: An Energy-Efficient Functional Unit for Artificial Neural Networks