Next Article in Journal
Numerical Analysis and Experimental Studies on the Residual Stress of W/2024Al Composites
Previous Article in Journal
The Usability of Pumice Powder as a Binding Additive in the Aspect of Selected Mechanical Parameters for Concrete Road Pavement
Previous Article in Special Issue
On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits
Open AccessReview

Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations

Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Universidad de Sevilla, 41092 Sevilla, Spain
*
Author to whom correspondence should be addressed.
Materials 2019, 12(17), 2745; https://doi.org/10.3390/ma12172745
Received: 5 July 2019 / Revised: 2 August 2019 / Accepted: 10 August 2019 / Published: 27 August 2019
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems. View Full-Text
Keywords: neuromorphic systems; spiking neural networks; memristors; spike-timing-dependent plasticity neuromorphic systems; spiking neural networks; memristors; spike-timing-dependent plasticity
Show Figures

Figure 1

MDPI and ACS Style

Camuñas-Mesa, L.A.; Linares-Barranco, B.; Serrano-Gotarredona, T. Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations. Materials 2019, 12, 2745.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop