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A system implementing reinforcement learning the system comprising a computer processor and a computer readable medium having computer executable instructions executed by said processor; said computer readable medium including instructions for providing: an action values module that receives environmental state as input, containing at least one adaptive element that learns state and/or action values based on an error signal; an action selection module coupled to the action values module; and an error calculation module coupled to both the action values and action selection module, which computes an error signal used to update state and/or action values in the action values module.


A system implementing reinforcement learning the system comprising a computer processor and a computer readable medium having computer executable instructions executed by said processor; said computer readable medium including instructions for providing: an action values module that receives environmental state as input, containing at least one adaptive element that learns state and/or action values based on an error signal; an action selection module coupled to the action values module; and an error calculation module coupled to both the action values and action selection module, which computes an error signal used to update state and/or action values in the action values module.


Patent
Applied Brain Research | Date: 2017-01-25

A method for implementing spiking neural network computations, the method including defining a dynamic node response function that exhibits spikes, where spikes are temporal nonlinearities for representing state over time; defining a static representation of said node response function; and using the static representation of the node response function to train a neural network. A system for implementing the method is also disclosed.


The 2015 R&D 100 Awards Banquet took place on November 13, 2015 at Caesars Palace in Las Vegas, Nevada and welcomed hundreds of executives, scientists and researchers. Celebrating the event’s 53rd year, leaders of science and technology were honored for their innovative, high-tech products and processes. The R&D 100 Awards is an international competition that recognizes the 100 most technologically significant products introduced into the marketplace over the past year, recognizing excellence across a wide range of industries, including telecommunications, optics, high-energy physics, materials science, chemistry and biotechnology. The full list of 2015 R&D 100 Award Winners is available here: www.rdmag.com/news/2015/11/2015-r-d-100-award-winners. This year, the R&D 100 Awards also included a brand-new Special Recognition Awards category, as well as a two-day science and technology conference focused on the theme of innovation. Gold, Silver and Bronze Awards for the Special Recognition Awards were announced during the ceremony for Green Tech, Corporate Social Responsibility, Market Disruptor Services and Market Disruptor Product. The R&D 100 Green Tech Special Recognition Gold Award went to the CO2 Memzyme by Sandia National Laboratories and the Univ. of New Mexico; the Silver Award was presented to Dow UF PURINZE and Its Application in Eco Washing Machine by Dow Water and Process Solution; and the Bronze Award went to the High Efficient and Reliable Ocean Wave Energy Harvester by Virginia Polytechnic Institute and State Univ. The Corporate Social Responsibility Special Recognition Gold Award was presented to the KM-CDR Process by Southern Company and Mitsubishi Heavy Industries and the Silver Award went to the Echo-Screen III Hearing Screener by Natus Medical Inc. The Market Disruptor Services Special Recognition Gold Award went to the ELUTE Fiber by TissueGen Inc., while the Silver Award was presented to the Infrared Nanodestructive Wield Examination System by Oak Ridge National Laboratory. The Market Disruptor Product Special Recognition Gold Award was presented to HeliAct Muscles by Lintec and the Univ. of Texas at Dallas; the Silver Award went to the Solaris Open Air Fluorescence Imaging System by PerkinElmer; and the Bronze Award was awarded to Airyscan by Carl Zeiss Microscopy. “These special awards, new this year, highlight the incredible innovations that will have a direct impact on people’s daily lives, from health care to energy technologies, and demonstrate just how the pace of research and development affects how we live,” said Bea Riemschneider, Editorial Director of the Advantage Business Media Science Group, the parent company of the R&D 100 Awards and R&D Magazine. The much-anticipated R&D Magazine Editor’s Choice Awards concluded the R&D 100 gala event as the winners came on stage to accept the crystal awards from Advantage Business Media CEO Jim Lonergan. This year, one winner was announced from each of the five R&D 100 Award categories—Analytical/Test, IT/Electrical, Mechanical/Materials, Process/Prototyping and Software/Services. “These five technologies had the ‘wow’ factor that our editors look for each year among the R&D 100 Awards, and they demonstrated once again the creativity and innovation in the marketplace,” said R&D Editor Lindsay Hock. The R&D Magazine’s Editor’s Choice Award in Analytical/Test went to Thermo Fisher Scientific’s Thermo Scientific Gemni analyzer. The IT/Electrical Editor’s Choice Award was presented to IBM’s TrueNorth neurosynaptic chip, and the Mechanical/Materials award went to National Renewable Energy Laboratory’s cyanobacterial bioethylene. The Process/Prototyping Editor’s Choice Award was awarded to Cincinnati Incorporated and Oak Ridge National Laboratory’s BAAM-CI, while the Software/Services award went to Applied Brain Research’s Nengo 2.0. Next year’s R&D 100 Awards will take place November 4, 2016, at the Gaylord National Resort and Convention Center in the National Harbor in Maryland in conjunction with the second annual R&D 100 Awards & Technology Conference, scheduled for November 3-4, 2016. Entries for the 2016 R&D 100 Awards are now open. Please go to www.rd100awards.com for more information.


News Article | November 11, 2016
Site: www.newsmaker.com.au

According to the new research report "Neuromorphic Computing Market by Offering (Hardware, Software), Application (Image Recognition, Signal Recognition, Data Mining), Industry (Aerospace & Defense, IT & Telecom, Automotive, Medical & Industrial) and Geography - Global Forecast to 2022", the neuromorphic computing market is expected to grow from USD 6.6 Million in 2016 to USD 272.9 Million by 2022, at a CAGR of 86.0% during the forecast period. The factors driving the market growth include increasing demand for artificial intelligence and machine learning, need for better performing ICs, and new ways of computation due to the end of Moore’s law. Browse 70 market data Tables and 68 Figures spread through 163 Pages and in-depth TOC on "Neuromorphic Computing Market". Early buyers will receive 10% customization on this report. Software to hold the largest share of the neuromorphic computing market during the forecast period Software is expected to hold the largest share of 58.0% of the neuromorphic computing market, based on offering, in 2016. Software has applications in video monitoring, machine vision, and voice identification. Increasing adoption of software in industries such as aerospace & defense, IT & telecom, and medical is driving the growth of the neuromorphic computing software market. Signal recognition applications to drive the growth of the neuromorphic computing market between 2016 and 2022 Adoption of speech recognition in industries such as medical and automotive & transportation is another major factor driving the market growth. For instance, speech recognition is used in the field of healthcare for radiologist scanning of hundreds of X-rays, ultra-sonograms, CT scans, and simultaneously dictating conclusions to a speech recognition system connected to the word processors. North America to hold the major share of the neuromorphic computing market during the forecast period North America, which comprises the U.S., Mexico, and Canada, is expected to hold the major share of the global neuromorphic computing market between 2016 and 2022. The market in this region is likely to grow at the highest rate during the forecast period since North America is one of the major markets for image recognition. Also, the higher penetration of devices with unique voice and image identification capabilities in defense, wearables, IoT, and robotics technology for interactive experience is driving the growth of the neuromorphic computing market in North America. The major players in the neuromorphic computing market include IBM Corporation (U.S.); HP Corp. (U.S.); Samsung Electronics Ltd. (South Korea); Intel Corp. (U.S.); Qualcomm Inc. (U.S.); HRL Laboratories, LLC (U.S.); General Vision Inc. (U.S.); Applied Brain Research, Inc. (U.S.); and BrainChip Holdings Ltd. (U.S.). MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model – GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M’s flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical infographics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository. Contact: Mr.Rohan MarketsandMarkets 701 Pike Street  Suite 2175, Seattle,  WA 98101, United States  Tel : 1-888-600-6441 Email: [email protected] Visit MarketsandMarkets Blog@ http://www.marketsandmarketsblog.com/market-reports/electronics-and-semiconductors  Connect with us on LinkedIn @ http://www.linkedin.com/company/marketsandmarkets Website: http://www.marketsandmarkets.com/


Non-syndromic autosomal recessive intellectual disability (ID) is a genetically heterogeneous disorder with more than 50 mutated genes to date. ID is characterized by deficits in memory skills and language development with difficulty in learning, problem solving, and adaptive behaviors, and affects ∼1% of the population. For detection of disease-causing mutations in such a heterogeneous disorder, homozygosity mapping together with exome sequencing is a powerful approach, as almost all known genes can be assessed simultaneously in a high-throughput manner. In this study, a hemizygous c.786C>G:p.Ile262Met in the testis specific protein Y-encoded-like 2 (TSPYL2) gene and a homozygous c.11335G>A:p.Asp3779Asn in the low-density lipoprotein receptor-related protein 2 (LRP2) gene were detected after genome-wide genotyping and exome sequencing in a consanguineous Pakistani family with two boys with mild ID. Mutations in the LRP2 gene have previously been reported in patients with Donnai–Barrow and Stickler syndromes. LRP2 has also been associated with a 2q locus for autism (AUTS5). The TSPYL2 variant is not listed in any single-nucleotide polymorphism databases, and the LRP2 variant was absent in 400 ethnically matched healthy control chromosomes, and is not listed in single-nucleotide polymorphism databases as a common polymorphism. The LRP2 mutation identified here is located in one of the low-density lipoprotein-receptor class A domains, which is a cysteine-rich repeat that plays a central role in mammalian cholesterol metabolism, suggesting that alteration of cholesterol processing pathway can contribute to ID. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.


Patent
Applied Brain Research | Date: 2015-07-23

A method for implementing spiking neural network computations, the method including defining a dynamic node response function that exhibits spikes, where spikes are temporal nonlinearities for representing state over time; defining a static representation of said node response function; and using the static representation of the node response function to train a neural network. A system for implementing the method is also disclosed.


Patent
Applied Brain Research | Date: 2015-11-10

Methods, systems and methods for designing a system that provides adaptive control and adaptive predictive filtering using nonlinear components. A system design is described that provides an engineered architecture. This architecture defines a core set of network dynamics that carry out specific functions related to control or prediction. The adaptation systems and methods can be applied to limited areas of he system to allow the system to learn to compensate for unmodeled system dynamics and kinematics. Two types of adaptive modules are described which are configured to account for the unmodeled system dynamics and kinematics.


Patent
Applied Brain Research | Date: 2013-12-02

Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representationsreferred to as semantic pointerswhich model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

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