Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

Experimental study of artificial neural networks using a digital memristor simulator

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft

Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

Adaptive optical networks using photorefractive crystals

The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures

Neural Network Models of Learning and Memory: Leading Questions and an Emerging Framework

Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (NIH 20-316-4304-5