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Overcoming phase-change material non-idealities by meta-learning for adaptation on the edge
Overcoming phase-change material non-idealities by meta-learning for adaptation on the edge
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Conference Proceeding 		Add to My Research

Overcoming phase-change material non-idealities by meta-learning for adaptation on the edge

DOI: 10.29363/nanoge.neumatdecas.2023.043 ;DOI: 10.5167/uzh-254205

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2
Training Spiking Neural Networks Using Lessons From Deep Learning
Training Spiking Neural Networks Using Lessons From Deep Learning
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Article 		Add to My Research

Training Spiking Neural Networks Using Lessons From Deep Learning

Proceedings of the IEEE, 2023-09, Vol.111 (9), p.1016-1054 [Peer Reviewed Journal]

ISSN: 0018-9219 ;EISSN: 1558-2256 ;DOI: 10.1109/JPROC.2023.3308088 ;CODEN: IEEPAD

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3
Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing
Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing
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Article 		Add to My Research

Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing

Frontiers in neuroscience, 2020-04, Vol.14, p.276-276 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2020 Li, Deng, Chua, Li, Neftci and Li. 2020 Li, Deng, Chua, Li, Neftci and Li ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00276 ;PMID: 32351350

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4
Nonlinear computations in spiking neural networks through multiplicative synapses
Nonlinear computations in spiking neural networks through multiplicative synapses
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Article 		Add to My Research

Nonlinear computations in spiking neural networks through multiplicative synapses

Peer community journal, 2021-12, Vol.1, Article e68 [Peer Reviewed Journal]

ISSN: 2804-3871 ;EISSN: 2804-3871 ;DOI: 10.24072/pcjournal.69

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5
Data and Power Efficient Intelligence with Neuromorphic Learning Machines
Data and Power Efficient Intelligence with Neuromorphic Learning Machines
Material Type:
Article 		Add to My Research

Data and Power Efficient Intelligence with Neuromorphic Learning Machines

iScience, 2018-07, Vol.5, p.52-68 [Peer Reviewed Journal]

2018 The Author ;Copyright © 2018 The Author. Published by Elsevier Inc. All rights reserved. ;2018 The Author 2018 ;ISSN: 2589-0042 ;EISSN: 2589-0042 ;DOI: 10.1016/j.isci.2018.06.010 ;PMID: 30240646

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6
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
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Article 		Add to My Research

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

Frontiers in neuroscience, 2017-06, Vol.11, p.324-324 [Peer Reviewed Journal]

2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2017 Neftci, Augustine, Paul and Detorakis. 2017 Neftci, Augustine, Paul and Detorakis ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2017.00324 ;PMID: 28680387

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7
Reinforcement learning in artificial and biological systems
Reinforcement learning in artificial and biological systems
Material Type:
Article 		Add to My Research

Reinforcement learning in artificial and biological systems

Nature machine intelligence, 2019-03, Vol.1 (3), p.133-143 [Peer Reviewed Journal]

This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019. ;ISSN: 2522-5839 ;EISSN: 2522-5839 ;DOI: 10.1038/s42256-019-0025-4

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8
Surrogate Gradient Learning in Spiking Neural Networks
Surrogate Gradient Learning in Spiking Neural Networks
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Article 		Add to My Research

Surrogate Gradient Learning in Spiking Neural Networks

arXiv.org, 2019-05

2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.1901.09948

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9
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
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Article 		Add to My Research

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

arXiv.org, 2016-12

2016. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.1511.04484

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10
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Material Type:
Article 		Add to My Research

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Frontiers in neuroscience, 2016-06, Vol.10, p.241-241 [Peer Reviewed Journal]

2016. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2016 Neftci, Pedroni, Joshi, Al-Shedivat and Cauwenberghs. 2016 Neftci, Pedroni, Joshi, Al-Shedivat and Cauwenberghs ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2016.00241 ;PMID: 27445650

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11
Dynamic state and parameter estimation applied to neuromorphic systems
Dynamic state and parameter estimation applied to neuromorphic systems
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Article 		Add to My Research

Dynamic state and parameter estimation applied to neuromorphic systems

ISSN: 0899-7667 ;EISSN: 1530-888X ;DOI: 10.5167/uzh-75345 ;DOI: 10.1162/NECO_a_00293

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12
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
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Article 		Add to My Research

On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks

arXiv.org, 2020-11

2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2011.10852

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13
On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks
On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks
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Article 		Add to My Research

On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks

ISSN: 2156-3357 ;EISSN: 2156-3365 ;DOI: 10.1109/jetcas.2020.3040248 ;DOI: 10.5167/uzh-200399

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14
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
Material Type:
Conference Proceeding 		Add to My Research

Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays

ISBN: 1728149223 ;ISBN: 9781728149226 ;DOI: 10.1109/aicas48895.2020.9073998 ;DOI: 10.5167/uzh-200398

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15
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
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Article 		Add to My Research

Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays

arXiv.org, 2019-10

2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.1910.06152

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16
Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity
Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity
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Article 		Add to My Research

Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity

Frontiers in neuroscience, 2019-04, Vol.13, p.357-357 [Peer Reviewed Journal]

2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2019 Pedroni, Joshi, Deiss, Sheik, Detorakis, Paul, Augustine, Neftci and Cauwenberghs. 2019 Pedroni, Joshi, Deiss, Sheik, Detorakis, Paul, Augustine, Neftci and Cauwenberghs ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2019.00357 ;PMID: 31110470

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17
ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware
ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware
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Article 		Add to My Research

ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware

arXiv.org, 2023-01

2023. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2301.08281

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18
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
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Article 		Add to My Research

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators

arXiv.org, 2020-03

2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2003.11639

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19
PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems
PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems
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Article 		Add to My Research

PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems

Frontiers in neuroinformatics, 2014-08, Vol.8, p.73-73 [Peer Reviewed Journal]

2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright © 2014 Stefanini, Neftci, Sheik and Indiveri. 2014 ;ISSN: 1662-5196 ;EISSN: 1662-5196 ;EISSN: 1662-453X ;DOI: 10.3389/fninf.2014.00073 ;PMID: 25232314

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20
PyNCS: A microkernel for high-level definition and configuration of neuromorphic electronic systems
PyNCS: A microkernel for high-level definition and configuration of neuromorphic electronic systems
Material Type:
Article 		Add to My Research

PyNCS: A microkernel for high-level definition and configuration of neuromorphic electronic systems

Creative Commons: Attribution 3.0 Unported (CC BY 3.0) http://creativecommons.org/licenses/by/3.0 ;ISSN: 1662-5196 ;EISSN: 1662-5196 ;DOI: 10.5167/uzh-107849 ;DOI: 10.3389/fninf.2014.00073

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