Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
1.6
Journals
JLPEA
Special Issues
Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms
share announcement

Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8671

Special Issue Editors

Dr. Huanglong Li

E-Mail Website
Guest Editor
1. Department of Precision Instrument, Tsinghua University, Beijing 100084, China
2. Chinese Institute for Brain Research, Beijing 102206, China
Interests: nanoelectronics; neuromorphic computing; electronic materials and ab initio calculations.
Prof. Dr. Si Wu

E-Mail Website
Guest Editor
School of Psychology and Cognitive Sciences, IDG/McGovern Institute for Brain Research, PKU-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
Interests: computational neuroscience; brain-inspired computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In collaboration with the Journal of Low Power Electronics and Applications, we are bringing together a selected group of international experts to contribute to a Special Issue on “Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms”.

Many neuroscientists believe that brains differ from conventional computers in ways that exacerbate the dependence of algorithms on hardware. It is absurdly difficult, if not impossible, to understand cognition without considering its implementations. Fourteen years ago, HP Labs claimed the discovery of the memristor device, which has been regarded as one of the most appropriate neuromorphs for brain-inspired computing and bio-explainable artificial intelligence. Despite the enormous research interest in memristive neuromorphics and the many significant advances in this field, most observers would judge today’s memristive neuromorphic devices and circuits as still being in their infancy and lacking the sophistication and flexibility of their biological counterparts.

In this context, this Special Issue aims to bring together researchers working in directions including, but not limited to:

  • Novel neuromorphic devices with new operating principles (not just new materials) that serve as the more compact and bio-realistic embodiments of neuronal elements.
  • Novel neuromorphic devices or circuits for mimicking neuronal behaviors, especially those with a certain dynamic complexity.
  • Novel neuromorphic devices or circuits for implementing neural algorithms beyond vector‐matrix multiplication.
  • Device/circuit properties-inspired algorithms and their simulations or hardware implementations.

Dr. Huanglong Li
Prof. Dr. Si Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • memristor
  • neuromrophic computing
  • bio-fidelity
  • algorithm-hardware co-design

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

19 pages, 1050 KiB  
Article
Towards Low-Power Machine Learning Architectures Inspired by Brain Neuromodulatory Signalling
by Taylor Barton, Hao Yu, Kyle Rogers, Nancy Fulda, Shiuh-hua Wood Chiang, Jordan Yorgason and Karl F. Warnick
J. Low Power Electron. Appl. 2022, 12(4), 59; https://doi.org/10.3390/jlpea12040059 - 4 Nov 2022
Cited by 2 | Viewed by 2554
Abstract
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant and a new, similar task is learned by manipulating [...] Read more.
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant and a new, similar task is learned by manipulating the firing sensitivity of each neuron via a supplemental bias input. We refer to this as neuromodulatory tuning (NT). We demonstrate empirically that neuromodulatory tuning produces results comparable with traditional fine-tuning (TFT) methods in the domain of image recognition in both feed-forward deep learning and spiking neural network architectures. In our tests, NT reduced the number of parameters to be trained by four orders of magnitude as compared with traditional fine-tuning methods. We further demonstrate that neuromodulatory tuning can be implemented in analog hardware as a current source with a variable supply voltage. Our analog neuron design implements the leaky integrate-and-fire model with three bi-directional binary-scaled current sources comprising the synapse. Signals approximating modulatory neurotransmitter mechanisms are applied via adjustable power domains associated with each synapse. We validate the feasibility of the circuit design using high-fidelity simulation tools and propose an efficient implementation of neuromodulatory tuning using integrated analog circuits that consume significantly less power than digital hardware (GPU/CPU). Full article
(This article belongs to the Special Issue Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms)
►▼ Show Figures

Figure 1

14 pages, 2523 KiB  
Article
Intelligent Control of Seizure-Like Activity in a Memristive Neuromorphic Circuit Based on the Hodgkin–Huxley Model
by Wallace Moreira Bessa and Gabriel da Silva Lima
J. Low Power Electron. Appl. 2022, 12(4), 54; https://doi.org/10.3390/jlpea12040054 - 12 Oct 2022
Cited by 2 | Viewed by 2795
Abstract
Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile computer memory, neuroprosthetics, and brain–machine interfaces. However, due to [...] Read more.
Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile computer memory, neuroprosthetics, and brain–machine interfaces. However, due to their intrinsically nonlinear characteristics, they present a very complex dynamic behavior, including self-sustained oscillations, seizure-like events, and chaos, which may compromise their use in closed-loop systems. In this work, a novel intelligent controller is proposed to suppress seizure-like events in a memristive circuit based on the Hodgkin–Huxley equations. For this purpose, an adaptive neural network is adopted within a Lyapunov-based nonlinear control scheme to attenuate bursting dynamics in the circuit, while compensating for modeling uncertainties and external disturbances. The boundedness and convergence properties of the proposed control scheme are rigorously proved by means of a Lyapunov-like stability analysis. The obtained results confirm the effectiveness of the proposed intelligent controller, presenting a much improved performance when compared with a conventional nonlinear control scheme. Full article
(This article belongs to the Special Issue Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms)
►▼ Show Figures

Figure 1

Other

Jump to: Research

6 pages, 1479 KiB  
Brief Report
Direct-Grown Helical-Shaped Tungsten-Oxide-Based Devices with Reconfigurable Selectivity for Memory Applications
by Ying-Chen Chen, Yifu Huang, Sumant Sarkar, John Gibbs and Jack Lee
J. Low Power Electron. Appl. 2022, 12(4), 55; https://doi.org/10.3390/jlpea12040055 - 15 Oct 2022
Cited by 1 | Viewed by 2361
Abstract
In this study, a direct-grown helical-shaped tungsten-oxide-based (h-WOx) selection device is presented for emerging memory applications. The selectivity in the selection devices is from 10 to 103 with a low off-current of 0.1 to 0.01 nA. In addition, the selectivity [...] Read more.
In this study, a direct-grown helical-shaped tungsten-oxide-based (h-WOx) selection device is presented for emerging memory applications. The selectivity in the selection devices is from 10 to 103 with a low off-current of 0.1 to 0.01 nA. In addition, the selectivity of volatile switching in the h-WOx selection devices is reconfigurable with a pseudo RESET process on the one-time negative voltage operations. The helical-shaped selection devices with the glancing angle deposition (GLAD) method show good compatibility, low power consumption, good selectivity, and good reconfigurability for next-generation memory applications. Full article
(This article belongs to the Special Issue Memristive Neuromorphics: Neuronal Emulators and Hardware Implementations of Neural Algorithms)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop