Neuromorphic Engineering and Machine Learning

Dear Colleagues,

Neuromorphic engineering proposes the use of biologically plausible models applied to engineering applications. The brain has been proven to solve complex problems in the most efficient way thanks to evolution. Therefore, replicating the way in which the brain performs specific tasks has become an interesting and promising field of research.

This Special Issue focuses on recent advances in neuromorphic engineering, including neuromorphic sensors, neuromorphic systems, bio-inspired models, spiking neural networks, and machine learning, which consider both hardware (digital or analog circuits) and software implementations.

Submissions to this Special Issue on ‘Neuromorphic Engineering and Machine Learning’ are solicited to represent a snapshot of the field’s development by covering a range of topics such as (but not limited to) the following:

Event-based sensors: vision, audio, tactile, olfactory;

Spiking neural network models;

Spike-based central pattern generators;

Machine learning applied to spike-based systems;

Deep learning algorithms for neuromorphic applications.

Dr. Juan P. Domínguez-Morales
Dr. Fernando Perez-Peña
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. Information 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 1600 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 engineering
• spiking neural networks
• neuromorphic sensors
• machine learning
• deep learning
• address-event representation