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Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
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Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades

Frontiers in neuroscience, 2015-11, Vol.9, p.437-437 [Peer Reviewed Journal]

2015. 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 © 2015 Orchard, Jayawant, Cohen and Thakor. 2015 Orchard, Jayawant, Cohen and Thakor ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2015.00437 ;PMID: 26635513

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2
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
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Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks

Frontiers in neuroscience, 2018-05, Vol.12, p.331-331 [Peer Reviewed Journal]

2018. 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 © 2018 Wu, Deng, Li, Zhu and Shi. 2018 Wu, Deng, Li, Zhu and Shi ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2018.00331 ;PMID: 29875621

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3
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
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Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

Frontiers in neuroscience, 2017-12, Vol.11, p.682-682 [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 Rueckauer, Lungu, Hu, Pfeiffer and Liu. 2017 Rueckauer, Lungu, Hu, Pfeiffer and Liu ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2017.00682 ;PMID: 29375284

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4
Deep Learning With Spiking Neurons: Opportunities and Challenges
Deep Learning With Spiking Neurons: Opportunities and Challenges
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Deep Learning With Spiking Neurons: Opportunities and Challenges

Frontiers in neuroscience, 2018-10, Vol.12, p.774-774 [Peer Reviewed Journal]

2018. 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 © 2018 Pfeiffer and Pfeil. 2018 Pfeiffer and Pfeil ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2018.00774 ;PMID: 30410432

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5
Training Deep Spiking Neural Networks Using Backpropagation
Training Deep Spiking Neural Networks Using Backpropagation
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Training Deep Spiking Neural Networks Using Backpropagation

Frontiers in neuroscience, 2016-11, Vol.10, p.508-508 [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 Lee, Delbruck and Pfeiffer. 2016 Lee, Delbruck and Pfeiffer ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2016.00508 ;PMID: 27877107

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6
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
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Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)

Frontiers in neuroscience, 2020-05, Vol.14, p.424-424 [Peer Reviewed Journal]

Copyright © 2020 Kaiser, Mostafa and Neftci. ;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 Kaiser, Mostafa and Neftci. 2020 Kaiser, Mostafa and Neftci ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00424 ;PMID: 32477050

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7
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
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Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks

Frontiers in neuroscience, 2020-05, Vol.14, p.439-439 [Peer Reviewed Journal]

Copyright © 2020 Kugele, Pfeil, Pfeiffer and Chicca. ;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 Kugele, Pfeil, Pfeiffer and Chicca. 2020 Kugele, Pfeil, Pfeiffer and Chicca ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00439 ;PMID: 32431592

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8
Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
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Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Frontiers in neuroscience, 2018-12, Vol.12, p.891-891 [Peer Reviewed Journal]

2018. 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 © 2018 Thakur, Molin, Cauwenberghs, Indiveri, Kumar, Qiao, Schemmel, Wang, Chicca, Olson Hasler, Seo, Yu, Cao, van Schaik and Etienne-Cummings. 2018 Thakur, Molin, Cauwenberghs, Indiveri, Kumar, Qiao, Schemmel, Wang, Chicca, Olson Hasler, Seo, Yu, Cao, van Schaik and Etienne-Cummings ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2018.00891 ;PMID: 30559644

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9
Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems
Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems
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Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

Frontiers in neuroscience, 2021-03, Vol.15, p.638474-638474 [Peer Reviewed Journal]

Copyright © 2021 Guo, Fouda, Eltawil and Salama. ;2021. 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 © 2021 Guo, Fouda, Eltawil and Salama. 2021 Guo, Fouda, Eltawil and Salama ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2021.638474 ;PMID: 33746705

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10
Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics
Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics
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Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

Frontiers in neuroscience, 2020-04, Vol.14, p.358-358 [Peer Reviewed Journal]

Copyright © 2020 Mikhaylov, Pimashkin, Pigareva, Gerasimova, Gryaznov, Shchanikov, Zuev, Talanov, Lavrov, Demin, Erokhin, Lobov, Mukhina, Kazantsev, Wu and Spagnolo. ;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 Mikhaylov, Pimashkin, Pigareva, Gerasimova, Gryaznov, Shchanikov, Zuev, Talanov, Lavrov, Demin, Erokhin, Lobov, Mukhina, Kazantsev, Wu and Spagnolo. 2020 Mikhaylov, Pimashkin, Pigareva, Gerasimova, Gryaznov, Shchanikov, Zuev, Talanov, Lavrov, Demin, Erokhin, Lobov, Mukhina, Kazantsev, Wu and Spagnolo ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00358 ;PMID: 32410943

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11
Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization
Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization
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Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization

Frontiers in neuroscience, 2020-06, Vol.14, p.653-653 [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 Panda, Aketi and Roy. 2020 Panda, Aketi and Roy ;ISSN: 1662-453X ;ISSN: 1662-4548 ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00653 ;PMID: 32694977

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12
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

Frontiers in neuroscience, 2015-04, Vol.9, p.141-141 [Peer Reviewed Journal]

2015. 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 © 2015 Qiao, Mostafa, Corradi, Osswald, Stefanini, Sumislawska and Indiveri. 2015 ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2015.00141 ;PMID: 25972778

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13
Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning
Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning
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Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning

Frontiers in neuroscience, 2018-08, Vol.12, p.435-435 [Peer Reviewed Journal]

2018. 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 © 2018 Lee, Panda, Srinivasan and Roy. 2018 Lee, Panda, Srinivasan and Roy ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2018.00435 ;PMID: 30123103

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14
Plasticity in memristive devices for spiking neural networks
Plasticity in memristive devices for spiking neural networks
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Plasticity in memristive devices for spiking neural networks

Frontiers in neuroscience, 2015-03, Vol.9, p.51-51 [Peer Reviewed Journal]

2015. 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. ;Attribution ;Copyright © 2015 Saïghi, Mayr, Serrano-Gotarredona, Schmidt, Lecerf, Tomas, Grollier, Boyn, Vincent, Querlioz, La Barbera, Alibart, Vuillaume, Bichler, Gamrat and Linares-Barranco. 2015 ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2015.00051 ;PMID: 25784849

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15
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
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Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Frontiers in neuroscience, 2019-03, Vol.13, p.260-260 [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 Wunderlich, Kungl, Müller, Hartel, Stradmann, Aamir, Grübl, Heimbrecht, Schreiber, Stöckel, Pehle, Billaudelle, Kiene, Mauch, Schemmel, Meier and Petrovici. 2019 Wunderlich, Kungl, Müller, Hartel, Stradmann, Aamir, Grübl, Heimbrecht, Schreiber, Stöckel, Pehle, Billaudelle, Kiene, Mauch, Schemmel, Meier and Petrovici ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2019.00260 ;PMID: 30971881

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16
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
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Comparison of Artificial and Spiking Neural Networks on Digital Hardware

Frontiers in neuroscience, 2021-04, Vol.15, p.651141-651141 [Peer Reviewed Journal]

Copyright © 2021 Davidson and Furber. ;2021. 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 © 2021 Davidson and Furber. 2021 Davidson and Furber ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2021.651141 ;PMID: 33889071

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17
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
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SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Frontiers in neuroscience, 2019-07, Vol.13, p.625-625 [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. ;Distributed under a Creative Commons Attribution 4.0 International License ;Copyright © 2019 Mozafari, Ganjtabesh, Nowzari-Dalini and Masquelier. 2019 Mozafari, Ganjtabesh, Nowzari-Dalini and Masquelier ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2019.00625 ;PMID: 31354403

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18
μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks
μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks
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μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

Frontiers in neuroscience, 2021-05, Vol.15, p.664208-664208 [Peer Reviewed Journal]

2021. 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 © 2021 Stuijt, Sifalakis, Yousefzadeh and Corradi. 2021 Stuijt, Sifalakis, Yousefzadeh and Corradi ;ISSN: 1662-453X ;ISSN: 1662-4548 ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2021.664208 ;PMID: 34093116

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19
Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing
Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing
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Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

Frontiers in neuroscience, 2020-08, Vol.14 (520438), p.637-637 [Peer Reviewed Journal]

Copyright © 2020 Ceolini, Frenkel, Shrestha, Taverni, Khacef, Payvand and Donati. ;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. ;Distributed under a Creative Commons Attribution 4.0 International License ;Copyright © 2020 Ceolini, Frenkel, Shrestha, Taverni, Khacef, Payvand and Donati. 2020 Ceolini, Frenkel, Shrestha, Taverni, Khacef, Payvand and Donati ;ISSN: 1662-4548 ;ISSN: 1662-453X ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2020.00637 ;PMID: 32903824

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20
Neuromorphic silicon neuron circuits
Neuromorphic silicon neuron circuits
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Neuromorphic silicon neuron circuits

Frontiers in neuroscience, 2011-01, Vol.5, p.73-73 [Peer Reviewed Journal]

2011. Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the associated terms available at https://www.frontiersin.org/articles/10.3389/fnins.2011.00073 . ;Distributed under a Creative Commons Attribution 4.0 International License ;Copyright © 2011 Indiveri, Linares-Barranco, Hamilton, van Schaik, Etienne-Cummings, Delbruck, Liu, Dudek, Häfliger, Renaud, Schemmel, Cauwenberghs, Arthur, Hynna, Folowosele, Saïghi, Serrano-Gotarredona, Wijekoon, Wang and Boahen. 2011 ;ISSN: 1662-4548 ;EISSN: 1662-453X ;DOI: 10.3389/fnins.2011.00073 ;PMID: 21747754

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