Special Issue "Memristive Neural Architectures and Intelligent Systems"

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 354

Special Issue Editors

Digital University Kerala, Thiruvananthapuram, India
Interests: memristive computing; neural computing; analog circuits; in-memory computing; imaging applications; NLP applications
Prof. Dr. Bhaskar Choubey
E-Mail Website
Guest Editor
Analogue Circuits and Image Sensors, Siegen University, Siegen, Germany
Interests: image sensors; analog IC
Institut für Grundlagen der Elektrotechnik und Elektronik, Technische Universität Dresden, 01062 Dresden, Germany
Interests: nonlinear circuits and systems; bio-inspired computing; neuromorphic engineering; memristive and memcapacitive technologies; cellular neural/nonlinear/nanoscale networks; theory of complexity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuromorphic computing was inspired by the biological neural networks in the human brain. Neural architectures for neuromorphic computing can be made area-efficient with memristive devices and networks. Developing efficient hardware for learning and inference tasks is important for neural computing applications. Emerging devices used for building memristive systems often suffer from variability issues, making their implementation challenging.

The focus of this Special Issue is on the emerging devices, algorithms and systems that were inspired by the biological neural networks in the brain. Papers covering the latest research findings and reviews that highlight hardware implementations in memristors and neural computing, in-memory computing and neural networks, near-sensor neural networks, analog neural networks and sensor fusion, chaotic circuits and stochastic neural networks, cognitive architectures and their hardware implementations, neural circuits and ASIC, FPGA-based neural networks, hierarchal temporal networks, cellular neural networks and spiking neural networks are particularly sought after. Submissions should provide experimental evidence and results focusing on energy-efficient implementations of bio-inspired neural networks. Works that focus on algorithms need to at least cover embedded hardware or FPGA implementation. Works that detail the circuit or device level designs are also welcome if the energy and area efficiencies are reported.

Prof. Dr. Alex P James
Prof. Dr. Bhaskar Choubey
Dr. Alon Ascoli
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. Brain Sciences is an international peer-reviewed open access monthly 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 2000 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.


  • neuromorphic computing
  • memristor arrays
  • memristive computing
  • neural network hardware
  • in-memory computing
  • analog neural computing
  • neural cores in FPGA
  • neural cores in ASIC
  • variability aware neural networks

Published Papers

This special issue is now open for submission.
Back to TopTop