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Arxiv.org
by Giacomo Indiveri; Bernabe Linares-Barranco; Robert Legenstein; George Deligeorgis; Themistoklis Prodromakis
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Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in...
Source: http://arxiv.org/abs/1302.7007v1
PubMed Central
by Yu, Shimeng; Gao, Bin; Fang, Zheng; Yu, Hongyu; Kang, Jinfeng; Wong, H.-S. Philip
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This article is from Frontiers in Neuroscience , volume 7 . Abstract Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was...
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813892
PubMed Central
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This article is from Frontiers in Neuroscience , volume 7 . Abstract Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these...
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767911
Arxiv.org
by Thomas Pfeil; Andreas Grübl; Sebastian Jeltsch; Eric Müller; Paul Müller; Mihai A. Petrovici; Michael Schmuker; Daniel Brüderle; Johannes Schemmel; Karlheinz Meier
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In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional...
Source: http://arxiv.org/abs/1210.7083v4
Arxiv.org
by Alex Graves; Abdel-rahman Mohamed; Geoffrey Hinton
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Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so...
Source: http://arxiv.org/abs/1303.5778v1
PubMed Central
by Valov, I.; Linn, E.; Tappertzhofen, S.; Schmelzer, S.; van den Hurk, J.; Lentz, F.; Waser, R.
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This article is from Nature Communications , volume 4 . Abstract Redox-based nanoionic resistive memory cells are one of the most promising emerging nanodevices for future information technology with applications for memory, logic and neuromorphic computing. Recently, the serendipitous discovery of the link between redox-based nanoionic-resistive memory cells and memristors and memristive devices has further intensified the research in this field. Here we show on both a theoretical and an...
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3644102