Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

6. Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube

verfasst von : Nikola K. Kasabov

Erschienen in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter introduces brain-inspired evolving SNN (BI-SNN) in which both the SNN architecture and learning are inspired by the structure, organisation and learning in the human brain. BI-SNN manifest deep learning from data and deep knowledge representation inspired by human brain as discussed in Chap. 3 of the book. In BI-SNN data is represented as spikes, information is represented as spatio-temporal spike patterns and deep knowledge is represented as patterns of connections that are subject to deep learning and can be interpreted by humans.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Evolving Spiking Neural Networks
Nächstes Kapitel Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation
Literatur
1.
N. Kasabov, NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52(2014), 62–76 (2014)CrossRef
2.
N. Kasabov (ed.), Springer Handbook of Bio-/Neuroinformatics (Springer, Berlin, 2014)
3.
S.M. Bohte, The evidence for neural information processing with precise spike-times: a survey. Nat. Comput. 3 (2004)
4.
5.
P. Lichtsteiner, T. Delbruck, A 64 × 64 AER logarithmic temporal derivative silicon retina. Res. Microelectron. Electron. 2, 202–205 (2005)
6.
N. Nuntalid, K. Dhoble, N. Kasabov, in EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network, LNCS, vol. 7062 (Springer, Heidelber, 2011), pp. 451–460
7.
E. Bullmore, O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)CrossRef
8.
V. Braitenberg, A. Schüz, Statistics and Geometry of Neuronal Connectivity (Springer, Berlin, 1998)CrossRef
9.
B. Hellweig, A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol. Cybern. 82, 111–121 (2000)CrossRef
10.
Z.J. Chen, Y. He, P. Rosa-Neto, J. Germann, A.C. Evans, Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb. Cortex 18, 2374–2381 (2008)CrossRef
11.
N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach (Springer, London, 2007) (first edition 2002)
12.
N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw. 41, 188–201 (2013)CrossRef
13.
A. Mohemmed, S. Schliebs, S. Matsuda, N. Kasabov, SPAN: spike pattern association neuron for learning spatio-temporal sequences. Int. J. Neural Syst. 22(4), 1–16 (2012)CrossRef
14.
E. Tu, N. Kasabov, J. Yang, Mapping temporal variables into the NeuCube spiking neural network architecture for improved pattern recognition, predictive modelling and understanding of stream data. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1305–1317 (2017)
15.
N. Kasabov, NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals, in Artificial Neural Networks in Pattern Recognition (Springer, Heidelberg, 2012), pp. 225–243
16.
T. Kohonen, Self organising maps. Neural Comput. Appl. 7, 273–286 (1998) (Springer)
17.
J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U S A 79(1982), 2554–2558 (1982)MathSciNetCrossRef
18.
T. Masquelier, R. Guyonneau, S.J. Thorpe, Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 2008(3), e1377 (2008)CrossRef
19.
Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster et al., Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004)
20.
J. Shrager, T. Hogg, B.A. Huberman, Observation of phase transitions in spreading activation networks. Science 236(1987), 1092–1094 (1987)CrossRef
21.
D. Zhou, O. Bousquet, T.N. Lal, J. Weston, B. Schölkopf, Learning with local and global consistency. NIPS 2004, 595–602 (2004)
22.
N. Kasabov, Foundations of Neural Networks, fuzzy Systems and Knowledge Engineering (MIT Press, Cambridge, 1996)
23.
24.
25.
S. Marks, Immersive visualisation of 3-dimensional spiking neural networks. Evolving Syst. 8, 193–201 (2017)CrossRef
26.
N. Kasabov, V. Feigin, Z.G.Y.C. Hou, L. Liang, R. Krishnamurthi et al., Evolving spiking neural network method and systems for fast spatio-temporal pattern learning and classification and for early event prediction with a case study on stroke. Neurocomputing 134, 269–279 (2014)
27.
T. Delbruck, P. Lichtsteiner, Fast sensory motor control based on event-based hybrid neuromorphic-procedural system, in ISCAS 2007, New Orleans, LA, pp. 845–848 (2007)
28.
29.
N. Kasabov, E. Capecci, Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Inf. Sci. 294, 565–575 (2015)MathSciNetCrossRef
30.
S. Song, K.D. Miller, L.F. Abbott, Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(2000), 919–926 (2000)CrossRef
31.
K. Dhoble, N. Nuntalid, G. Indiveri, N. Kasabov, Online spatio-temporal pattern recognition with evolving spiking neural networks utilising address event representation, rank order, and temporal spike learning, in The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–7 (2012)
32.
J. Behrenbeck, Z. Tayeb, C. Bhiri, C. Richter, O. Rhodes, N. Kasabov, S. Furber, J. Conrad, Classification and Regression of Spatio-Temporal EMG Signals using NeuCube Spiking Neural Network and its implementation on SpiNNaker Neuromorphic Hardware. J. Neural Eng. (IOP Press, Article reference: JNE-102499) (2018). http://​iopscience.​iop.​org/​journal/​1741-2552
33.
S. Thorpe, J. Gautrais, Rank order coding, in Computational Neuroscience (Springer, New York, 1998), pp. 113–118
34.
M.G. Doborjeh, N. Kasabov, Z.G. Doborjeh, Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data. Evolving Syst. 1–17 (2017)
35.
N. Kasabov, N. Scott, E. Tu, S. Marks, N. Sengupta, E. Capecci, M. Othman, M. Doborjeh, N. Murli, R. Hartono, J. Espinosa-Ramos, L. Zhou, F. Alvi, G. Wang, D. Taylor, V. Feigin, S. Gulyaev, M. Mahmoudh, Z.-G. Hou, J. Yang, Design methodology and selected applications of evolving spatio-temporal data machines in the NeuCube neuromorphic framework. Neural Netw. 78, 1–14 (2016). https://​doi.​org/​10.​1016/​j.​neunet.​2015.​09.​011CrossRef
36.
N. Kasabov, Evolving connectionist systems: from neuro-fuzzy-, to spiking—and neurogenetic, in Springer Handbook of Computational Intelligence, ed. by J. Kacprzyk, W. Pedrycz (Springer, Berlin, 2015), pp. 771–782
Metadaten
Titel
Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube
verfasst von
Nikola K. Kasabov
Copyright-Jahr
2019
Verlag
Springer Berlin Heidelberg
Buch
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_6