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Neuromorphic Computing and Engineering is a peer-reviewed journal published by IOP Publishing, covering Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Neural Networks and Reservoir Computing. Over its lifetime it has published 362 papers, which have received 3,393 citations.
| Publisher | IOP Publishing |
|---|---|
| Reference style | Numbered (IOP) Numbered — [1], [2] in the text [1] Smith A, Jones B and Lee C 2023 A representative article title Neuromorphic Computing and Engineering 12 45–58
Formats any DOI in Neuromorphic Computing and Engineering style. No sign-up. |
| Publishes research in | Advanced Memory and Neural Computing Ferroelectric and Negative Capacitance Devices Neural Networks and Reservoir Computing Neural dynamics and brain function Neural Networks and Applications |
| ISSN | 2634-4386 |
| Citation impact (2-yr) | 3.17 |
| h-index | 27 |
| i10-index | 104 |
| Article processing charge | $2,453 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | iopscience.iop.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This manuscript serves a specific purpose: to give readers from fields such as material science, chemistry, or electronics an overview of implementing a reservoir computing (RC) experiment with her/his material system. Introductory literature on the topic is rare and the vast majority of reviews puts forth the basics of RC taking for granted concepts…
Abstract The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are…
Abstract With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory…
Abstract HfO 2 -based resistive switching memory (RRAM) combines several outstanding properties, such as high scalability, fast switching speed, low power, compatibility with complementary metal-oxide-semiconductor technology, with possible high-density or three-dimensional integration. Therefore, today, HfO 2 RRAMs have attracted a strong interest for applications in neuromorphic engineering, in particular for the development of artificial synapses…
Abstract Spiking neural networks (SNNs) have recently emerged as the low-power alternative to artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event-driven processing. Due to their energy efficiency, SNNs have a high possibility of being deployed for real-world, resource-constrained systems such as autonomous vehicles and drones. However, owing to their non-differentiable and…
