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[PDF] Top 20 Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

Has 10000 "Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices" found on our website. Below are the top 20 most common "Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices".

Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

... Immunity to Device Variations in a Spiking Neural Network with Memristive Nanodevices Damien Querlioz, Member, IEEE, Olivier Bichler, ... Voir le document complet

9

Simulation of a memristor-based spiking neural network immune to device variations

Simulation of a memristor-based spiking neural network immune to device variations

... the variations on the learning increments and decrements ( α + , α − ...These variations can be caused by variations of the device thresholds and of their programming ...the network is ... Voir le document complet

8

Plasticity in memristive devices for spiking neural networks

Plasticity in memristive devices for spiking neural networks

... well-associated to the concept of STM to LTM learning in psychology, we can note that it induces some restriction in term of network ...Indeed, in biol- ogy, the facilitating ... Voir le document complet

17

Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

... In a computational context, spiking neuron networks are mainly implemented through specific network architectures, such as Echo State Networks [25] and Liquid Sate Ma- chines [32], that are ... Voir le document complet

45

Memory-Centric Neuromorphic Computing With Nanodevices

Memory-Centric Neuromorphic Computing With Nanodevices

... implementing a neural network in hardware, with computation tightly integrated with memory, and all memory integrated ...investigated in the literature [4], [11], [12], ... Voir le document complet

5

Low-activity supervised convolutional spiking neural networks applied to speech commands recognition

Low-activity supervised convolutional spiking neural networks applied to speech commands recognition

... Deep Neural Networks (DNNs) are the current state- of-the-art models in many speech related ...is a growing interest, though, for more biologically re- alistic, hardware friendly and energy efficient ... Voir le document complet

8

Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits

Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits

... made to design a demonstrator of a smart vision sensor (European project ULPEC [8]) that co-integrates an event-based retinomorphic vision sensor [9] with a hardware spiking ... Voir le document complet

5

Spiking neural networks based on resistive memory technologies for neural data analysis

Spiking neural networks based on resistive memory technologies for neural data analysis

... allow to have a separation of LRS and HRS due to large resistance variability of both ...is a very promising ...coupled with a probabilistic learning rule (inspired by biological ... Voir le document complet

193

An Adaptive modular neural network with application to unconstrained character recognition

An Adaptive modular neural network with application to unconstrained character recognition

... As part of our research on unconstrained handwritten numeral recognition, we have developed a new adaptive modular network, which offers a high noise tolerance, shor[r] ... Voir le document complet

30

A Neural Network Demand System

A Neural Network Demand System

... HAL Id: halshs-00917810 https://halshs.archives-ouvertes.fr/halshs-00917810 Submitted on 12 Dec 2013 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research ... Voir le document complet

24

Back-engineering of spiking neural networks parameters

Back-engineering of spiking neural networks parameters

... the neural network model is a complex issue. In biological context, this learning mecha- nism is mainly related to synaptic weights plasticity and as far as spiking neural ... Voir le document complet

3

A Neural Network for Semigroups

A Neural Network for Semigroups

... reconstruction in computer vision, matrix completion in recommender systems and link prediction in graph theory, are well studied in machine learning ...literature. In this work, we ... Voir le document complet

13

EnaS: a new software for neural population analysis in large scale spiking networks

EnaS: a new software for neural population analysis in large scale spiking networks

... is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or ...institutions in France or abroad, or from public or ... Voir le document complet

3

Localisation based on Wi-Fi Fingerprints: A Crowdsensing Approach with a Device-to-Device Aim

Localisation based on Wi-Fi Fingerprints: A Crowdsensing Approach with a Device-to-Device Aim

... going to evaluate the performance of the localisation when relying on Wi-Fi threshold we defined earlier, more than one BSSID in common within two fin- ...close to zero meter error, then even ... Voir le document complet

6

Analysis of a neural network model

Analysis of a neural network model

... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ... Voir le document complet

20

Revisiting time discretisation of spiking network models

Revisiting time discretisation of spiking network models

... allows a better insight into the possible neural coding in such a network and provides a deep understanding, at the network level, of the system ...has a weak form ... Voir le document complet

3

A Comparative Study of Neural Network Compression

A Comparative Study of Neural Network Compression

... is a constant. As a consequence, in the later pruning steps, less edges are removed (the exact amount is controlled by c), and we have less dramatic accuracy drops (this is controlled by ...k). ... Voir le document complet

19

Dynamic Placement with Connectivity for RSNs based on a Primal-Dual Neural Network

Dynamic Placement with Connectivity for RSNs based on a Primal-Dual Neural Network

... 3 Neural network as a fast solver for linear quadratic programs The basic idea for solving an optimization problem using a tailored neural network is to make sure that the ... Voir le document complet

7

A new device to follow temporal variations of oxygen demand in deltaic sediments: the LSCE benthic station

A new device to follow temporal variations of oxygen demand in deltaic sediments: the LSCE benthic station

... steel with dimensions of 1.96 m × 1.90 m × 1.30 m. This frame was designed to minimize the interference between the benthic station and the current near the seabed to keep the erosion-deposition ... Voir le document complet

14

The committee machine: Computational to statistical gaps in learning a two-layers neural network

The committee machine: Computational to statistical gaps in learning a two-layers neural network

... on a two-layers neural network, but we note that the analysis and algorithm can be readily extended to a multi-layer setting, see [ 22 ], as long as the total number of hidden neurons ... Voir le document complet

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