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[PDF] Top 20 Simulation of a memristor-based spiking neural network immune to device variations

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

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

... issue of nanodevices is original and takes inspiration from recent works in computational neuroscience and neural networks ...exist to use memristive devices for STDP ...propose a simplified ... Voir le document complet

8

Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator

Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator

... by device-to-device variations and intrinsic device variability to a large extend [16], ...at a system level is becoming increasingly critical as it is more and ... Voir le document complet

7

Plasticity in memristive devices for spiking neural networks

Plasticity in memristive devices for spiking neural networks

... Symbol of memristor; (B) characteristic transport features of memristors: pinched iv loops for different values of the maximum injected ...in neural networks are not completely ... Voir le document complet

17

Neural activity of heterogeneous inhibitory spiking networks with delay

Neural activity of heterogeneous inhibitory spiking networks with delay

... group of networks with very low n A are characterized by a peculiar dynamics where only few neurons remain ac- ...example of this dynamics for g = 200 is reported in ...by a green arrow ... Voir le document complet

14

A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

... Spiking Neural Networks display promising characteristics for this paradigm change [23], [25], [30], [34], such as unsupervised training with STDP rules, which reduces the need for large annotated ... Voir le document complet

10

A Comparative Study of Neural Network Compression

A Comparative Study of Neural Network Compression

... Compared to pruning big chunks of edges at once, the purpose of reaching the desired compression by progressively pruning edges in multiple steps is to avoid sudden and unrecoverable accuracy ... Voir le document complet

19

A Neural Network for Semigroups

A Neural Network for Semigroups

... apply a denoising autoencoder-based neural network architecture to the task of completing partial multiplication (Cay- ley) tables of finite ...suggest a novel loss ... Voir le document complet

13

Neural network applications to reservoirs: Physics-based models and data models

Neural network applications to reservoirs: Physics-based models and data models

... applied a novel approach to sand production using ...index of sand production onset in oil and gas wells was aimed to be estimated through 4 proposed methods: (i) multiple linear regression ... Voir le document complet

15

Neural network stochastic simulation applied for quantifying uncertainties

Neural network stochastic simulation applied for quantifying uncertainties

... 3.2. SIMULATION The position of the selected points, rock types, magnetization, density contrast and neurons with a constant value of ...input to the neural network. In ... Voir le document complet

10

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

... Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and ... Voir le document complet

13

Role of synaptic variability in resistive memory-based spiking neural networks with unsupervised learning

Role of synaptic variability in resistive memory-based spiking neural networks with unsupervised learning

... impact of the conductance ...case of a synapse with zero variability (σG,LCS =0 and σG,HCS=0) (Figure 6 ...distribution to lower or higher conductance values. This allows to decouple ... Voir le document complet

13

A Neural Network Demand System

A Neural Network Demand System

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

24

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

... (ANR-10-LABX-0088) of Université de Lyon, within the program "Investissements d’Avenir" (ANR- 11-IDEX-0007) operated by the French National Research Agency ...obfuscation to preserve users’ privacy ... Voir le document complet

6

Inversion of Integral Models: a Neural Network Approach

Inversion of Integral Models: a Neural Network Approach

... All of the above strategies have shown very good numerical ...study of the robustness of such a structure is often based on a series of numerical tests, as performed in ... Voir le document complet

36

Simulation on Agent-based Onion Routing Network

Simulation on Agent-based Onion Routing Network

... variation of the number of the sender user agents does not have such an impact on the scalability of the onion routing network since the scalability relies on the receiver user ... Voir le document complet

11

Spatio-Temporal Linear Response of Spiking Neuronal Network Models

Spatio-Temporal Linear Response of Spiking Neuronal Network Models

... impact of a weak time- dependent external stimulus on the collective statistics of spiking responses in neuronal ...correlations, to any higher order spatio-temporal correlation ...is ... Voir le document complet

2

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

... perform a task alternating between beha- vioral adaptation –relying on feedback monitoring and memory of previous choices– and repetition of previous actions, firing rates in dorsal Anterior ... Voir le document complet

3

Neural network-based adaptive control for induction motors

Neural network-based adaptive control for induction motors

... The obtained controller is then augmented by an online single hidden layer neural network (SHL NN) that is used to adaptively compensate for the partially known dy[r] ... Voir le document complet

1

Revisiting time discretisation of spiking network models

Revisiting time discretisation of spiking network models

... free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours — you keep the ... Voir le document complet

3

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

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