Self-repairing learning rule for spiking astrocyte-neuron networks

Junxiu Liu; Liam J. McDaid; Jim Harkin; John J. Wade; Shvan Karim; Anju P. Johnson; Alan G. Millard; David M. Halliday; Andy M. Tyrrell; Jon Timmis

doi:10.1007/978-3-319-70136-3_41

Self-repairing learning rule for spiking astrocyte-neuron networks

Junxiu Liu^*, Liam J. McDaid, Jim Harkin, John J. Wade, Shvan Karim, Anju P. Johnson, Alan G. Millard, David M. Halliday, Andy M. Tyrrell, Jon Timmis

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

12 Citations (Scopus)

Abstract

In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.

Original language	English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Derong Liu, Shengli Xie, Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy
Publisher	Springer Nature
Pages	384-392
Number of pages	9
ISBN (Print)	9783319701356
DOIs	https://doi.org/10.1007/978-3-319-70136-3_41
Publication status	Published - 2017
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10639 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	14 Nov 2017 → 18 Nov 2017

Keywords

Astrocyte-neuron network
Fault tolerance
Learning window
Self-repair

Access to Document

10.1007/978-3-319-70136-3_41

Permanent link

Persistent link

Cite this

Liu, J., McDaid, L. J., Harkin, J., Wade, J. J., Karim, S., Johnson, A. P., Millard, A. G., Halliday, D. M., Tyrrell, A. M., & Timmis, J. (2017). Self-repairing learning rule for spiking astrocyte-neuron networks. In D. Liu, S. Xie, D. Zhao, Y. Li, & E.-S. M. El-Alfy (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 384-392). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10639 LNCS). Springer Nature. https://doi.org/10.1007/978-3-319-70136-3_41

Liu, Junxiu ; McDaid, Liam J. ; Harkin, Jim et al. / Self-repairing learning rule for spiking astrocyte-neuron networks. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Derong Liu ; Shengli Xie ; Dongbin Zhao ; Yuanqing Li ; El-Sayed M. El-Alfy. Springer Nature, 2017. pp. 384-392 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1b622be6f8c0482abe7b6e6ccd6adc51,

title = "Self-repairing learning rule for spiking astrocyte-neuron networks",

abstract = "In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.",

keywords = "Astrocyte-neuron network, Fault tolerance, Learning window, Self-repair",

author = "Junxiu Liu and McDaid, {Liam J.} and Jim Harkin and Wade, {John J.} and Shvan Karim and Johnson, {Anju P.} and Millard, {Alan G.} and Halliday, {David M.} and Tyrrell, {Andy M.} and Jon Timmis",

note = "Funding Information: Acknowledgments. This work is part of the EPSRC funded SPANNER (EP/N007141X/1) (EP/N007050/1). Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",

year = "2017",

doi = "10.1007/978-3-319-70136-3_41",

language = "English",

isbn = "9783319701356",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Nature",

pages = "384--392",

editor = "Derong Liu and Shengli Xie and Dongbin Zhao and Yuanqing Li and El-Alfy, {El-Sayed M.}",

booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",

address = "Switzerland",

}

Liu, J, McDaid, LJ, Harkin, J, Wade, JJ, Karim, S, Johnson, AP, Millard, AG, Halliday, DM, Tyrrell, AM & Timmis, J 2017, Self-repairing learning rule for spiking astrocyte-neuron networks. in D Liu, S Xie, D Zhao, Y Li & E-SM El-Alfy (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10639 LNCS, Springer Nature, pp. 384-392, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14 Nov 2017. https://doi.org/10.1007/978-3-319-70136-3_41

Self-repairing learning rule for spiking astrocyte-neuron networks. / Liu, Junxiu; McDaid, Liam J.; Harkin, Jim et al.
Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Derong Liu; Shengli Xie; Dongbin Zhao; Yuanqing Li; El-Sayed M. El-Alfy. Springer Nature, 2017. p. 384-392 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10639 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Self-repairing learning rule for spiking astrocyte-neuron networks

AU - Liu, Junxiu

AU - McDaid, Liam J.

AU - Harkin, Jim

AU - Wade, John J.

AU - Karim, Shvan

AU - Johnson, Anju P.

AU - Millard, Alan G.

AU - Halliday, David M.

AU - Tyrrell, Andy M.

AU - Timmis, Jon

N1 - Funding Information: Acknowledgments. This work is part of the EPSRC funded SPANNER (EP/N007141X/1) (EP/N007050/1). Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.

AB - In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.

KW - Astrocyte-neuron network

KW - Fault tolerance

KW - Learning window

KW - Self-repair

UR - http://www.scopus.com/inward/record.url?scp=85035117294&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-70136-3_41

DO - 10.1007/978-3-319-70136-3_41

M3 - Conference Proceeding (Non-Journal item)

AN - SCOPUS:85035117294

SN - 9783319701356

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 384

EP - 392

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Liu, Derong

A2 - Xie, Shengli

A2 - Zhao, Dongbin

A2 - Li, Yuanqing

A2 - El-Alfy, El-Sayed M.

PB - Springer Nature

T2 - 24th International Conference on Neural Information Processing, ICONIP 2017

Y2 - 14 November 2017 through 18 November 2017

ER -

Liu J, McDaid LJ, Harkin J, Wade JJ, Karim S, Johnson AP et al. Self-repairing learning rule for spiking astrocyte-neuron networks. In Liu D, Xie S, Zhao D, Li Y, El-Alfy ESM, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Nature. 2017. p. 384-392. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70136-3_41

Self-repairing learning rule for spiking astrocyte-neuron networks

Abstract

Publication series

Conference

Keywords

Access to Document

Permanent link

Other files and links

Fingerprint

Cite this