Optical Memory and Neural Networks
This journal covers a wide range of issues in information optics, such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical and optoelectronic components, and many other related topics.
It reports on theoretical researches and applications in different fields, including: data recording, processing and storage with the use of optical and holographic methods; associative memory (including biological memory), mathematical models and implementations of neuron systems for data processing and management; optoelectronic matrix devices and multiple-unit nanostructures for new data processing and storage systems.
The journal pays particular attention to research in the field of neural net systems that may, by Endowing computation means with intelligence, lead to a new generation of computing devices.PEER REVIEW
Optical Memory and Neural Networks is a peer reviewed journal. We use a single blind peer review format. Our team of reviewers includes 38 reviewers, both internal and external (75%), from 17 countries. The average period from submission to first decision in 2017 was 30 days, and that from first decision to acceptance was 30 days. The final decision on the acceptance of an article for publication is made by the Editors-in-Chief.
Any invited reviewer who feels unqualified or unable to review the manuscript due to the conflict of interests should promptly notify the editors and decline the invitation. Reviewers should formulate their statements clearly in a sound and reasoned way so that authors can use reviewer’s arguments to improve the manuscript. Personal criticism of the authors must be avoided. Reviewers should indicate in a review (i) any relevant published work that has not been cited by the authors, (ii) anything that has been reported in previous publications and not given appropriate reference or citation, (ii) any substantial similarity or overlap with any other manuscript (published or unpublished) of which they have personal knowledge.
P. Sh. Geidarov (April 2018)
Global Synchronization in the Finite Time for Variable-Order Fractional Neural Networks with Discontinuous Activations
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