Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons

Lyle N. Long

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper describes neural network algorithms and software that scale up to massively parallel computers. The neuron model used is the best available at this time, the Hodgkin-Huxley equations. Most massively parallel simulations use very simplified neuron models, which cannot accurately simulate biological neurons and the wide variety of neuron types. Using C++ and MPI we can scale these networks to human-level sizes. Computers such as the Chinese TianHe computer are capable of human level neural networks.

Original languageEnglish (US)
Title of host publicationArtificial General Intelligence - 9th International Conference, AGI 2016, Proceedings
EditorsBas Steunebrink, Pei Wang, Ben Goertzel
PublisherSpringer Verlag
Pages314-323
Number of pages10
ISBN (Print)9783319416489
DOIs
StatePublished - Jan 1 2016
Event9th International Conference on Artificial General Intelligence, AGI 2016 - New York, United States
Duration: Jul 16 2016Jul 19 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9782
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Artificial General Intelligence, AGI 2016
CountryUnited States
CityNew York
Period7/16/167/19/16

Fingerprint

Neuron Model
Neurons
Neuron
Neural Networks
Neural networks
Scale-up
Parallel Simulation
Network Algorithms
Parallel Computers
C++
Software
Human

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Long, L. N. (2016). Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons. In B. Steunebrink, P. Wang, & B. Goertzel (Eds.), Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings (pp. 314-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9782). Springer Verlag. https://doi.org/10.1007/978-3-319-41649-6_32
Long, Lyle N. / Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons. Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings. editor / Bas Steunebrink ; Pei Wang ; Ben Goertzel. Springer Verlag, 2016. pp. 314-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Long, LN 2016, Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons. in B Steunebrink, P Wang & B Goertzel (eds), Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9782, Springer Verlag, pp. 314-323, 9th International Conference on Artificial General Intelligence, AGI 2016, New York, United States, 7/16/16. https://doi.org/10.1007/978-3-319-41649-6_32

Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons. / Long, Lyle N.

Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings. ed. / Bas Steunebrink; Pei Wang; Ben Goertzel. Springer Verlag, 2016. p. 314-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9782).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Long LN. Toward human-level massively-parallel neural networks with Hodgkin-Huxley neurons. In Steunebrink B, Wang P, Goertzel B, editors, Artificial General Intelligence - 9th International Conference, AGI 2016, Proceedings. Springer Verlag. 2016. p. 314-323. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-41649-6_32