Convolutional networks for fast, energy-efficient neuromorphic computing. - Wikidata - awgldk - TriplyDB

Convolutional networks for fast, energy-efficient neuromorphic computing.

Convolutional networks for fast, energy-efficient neuromorphic computing.

http://www.wikidata.org/entity/Q30823621

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Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines Energy-efficient neural network chips approach human recognition capabilities.Implementing Signature Neural Networks with Spiking Neurons.Training Deep Spiking Neural Networks Using Backpropagation.Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.New challenge for bionics--brain-inspired computing.On-chip photonic synapse.Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture.Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems: The Network Core Triggers Fast Responses to Known Stimuli; Innovations Require the Slow Network Periphery and Are Encoded by Core-Remodeling.Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network.Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.Feature Representations for Neuromorphic Audio Spike Streams.Unsupervised Feature Learning With Winner-Takes-All Based STDP.A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth.A convolutional neural network-based screening tool for X-ray serial crystallography.Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks.Neurorobotics-A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots Deep Learning With Spiking Neurons: Opportunities and Challenges Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

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Convolutional networks for fast, energy-efficient neuromorphic computing.

http://www.wikidata.org/entity/Q30823621

description

2016 nî lūn-bûn

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2016 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

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2016 թվականի սեպտեմբերին հրատարակված գիտական հոդված

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2016年の論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年論文

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2016年论文

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name

Convolutional networks for fast, energy-efficient neuromorphic computing.

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Convolutional networks for fast, energy-efficient neuromorphic computing.

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Convolutional networks for fast, energy-efficient neuromorphic computing.

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Convolutional networks for fast, energy-efficient neuromorphic computing.

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Convolutional networks for fast, energy-efficient neuromorphic computing.

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PNAS.1604850113

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Proceedings of the National Academy of Sciences of the United States of America

P1476

Convolutional networks for fast, energy-efficient neuromorphic computing.

@en

P2093

Alexander Andreopoulos

http://www.w3.org/2001/XMLSchema#string

Andrew S Cassidy

http://www.w3.org/2001/XMLSchema#string

Arnon Amir

http://www.w3.org/2001/XMLSchema#string

Brian Taba

http://www.w3.org/2001/XMLSchema#string

Carmelo di Nolfo

http://www.w3.org/2001/XMLSchema#string

David J Berg

http://www.w3.org/2001/XMLSchema#string

Davis R Barch

http://www.w3.org/2001/XMLSchema#string

Dharmendra S Modha

http://www.w3.org/2001/XMLSchema#string

Jeffrey L McKinstry

http://www.w3.org/2001/XMLSchema#string

John V Arthur

http://www.w3.org/2001/XMLSchema#string

P2860

The "independent components" of natural scenes are edge filters

Learning representations by back-propagating errors

Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Six networks on a universal neuromorphic computing substrate.

An event-based neural network architecture with an asynchronous programmable synaptic memory.

Nanoconnectomic upper bound on the variability of synaptic plasticity.

Multi-column deep neural network for traffic sign classification.

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11441-11446

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1603.08270

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