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On the complexity of learning for a spiking neuron (extended abstract)

Authors:

Wolfgang Maass,

Michael SchmittAuthors Info & Claims

COLT '97: Proceedings of the tenth annual conference on Computational learning theory

Pages 54 - 61

https://doi.org/10.1145/267460.267477

Published: 01 July 1997 Publication History

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Mendez Lucero MKarampatsis RBojorquez Gallardo EBelle V(2022)Signal Perceptron: On the Identifiability of Boolean Function Spaces and BeyondFrontiers in Artificial Intelligence10.3389/frai.2022.7702545Online publication date: 2-Jun-2022
https://doi.org/10.3389/frai.2022.770254
Maass W(2019)On the relevance of time in neural computation and learningTheoretical Computer Science10.1016/S0304-3975(00)00137-7261:1(157-178)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/S0304-3975%2800%2900137-7
Shrestha SOrchard G(2018)SLAYERProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327073(1419-1428)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3326943.3327073
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On the complexity of learning for a spiking neuron (extended abstract)

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COLT '97: Proceedings of the tenth annual conference on Computational learning theory

July 1997

338 pages

ISBN:0897918916

DOI:10.1145/267460

Chairmen:
Yoav Freund
AT&T Labs
,
Robert Schapire
AT&T Labs

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 1997

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COLT97: Tenth Annual Conference on Computational Learning Theory

July 6 - 9, 1997

Tennessee, Nashville, USA

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Mendez Lucero MKarampatsis RBojorquez Gallardo EBelle V(2022)Signal Perceptron: On the Identifiability of Boolean Function Spaces and BeyondFrontiers in Artificial Intelligence10.3389/frai.2022.7702545Online publication date: 2-Jun-2022
https://doi.org/10.3389/frai.2022.770254
Maass W(2019)On the relevance of time in neural computation and learningTheoretical Computer Science10.1016/S0304-3975(00)00137-7261:1(157-178)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/S0304-3975%2800%2900137-7
Shrestha SOrchard G(2018)SLAYERProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327073(1419-1428)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3326943.3327073
Xue FZhang YZhou HLi X(2018)Effects of Temporal Integration on Computational Performance of Spiking Neural NetworkAdvances in Cognitive Neurodynamics (VI)10.1007/978-981-10-8854-4_16(127-133)Online publication date: 29-Jun-2018
https://doi.org/10.1007/978-981-10-8854-4_16
Maass WRuf B(1999)On Computation with PulsesInformation and Computation10.1006/inco.1998.2743148:2(202-218)Online publication date: Feb-1999
https://doi.org/10.1006/inco.1998.2743

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A spiking neuron model: applications and learning

Analogue Circuits of a Learning Spiking Neuron Model

Computationally EfficientBio-realistic Reconstructions of Cerebellar Neuron Spiking Patterns

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A spiking neuron model: applications and learning

Analogue Circuits of a Learning Spiking Neuron Model

Computationally EfficientBio-realistic Reconstructions of Cerebellar Neuron Spiking Patterns

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