Dynamic data-based modelling of synaptic plasticity

mGluR-dependent long-term depression

T. Tambuyzer, Tariq Ahmed, C. J. Taylor, D. Berckmans, D. Balschun, J. M. Aerts

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

Abstract

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR) dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked and it is believed that several crucial mechanisms still remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to the author's knowledge it is the first time that SI methods are applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse engineering of mGluR-LTD responses. It is suggested that such SI methods can aid to unravel the complexities of synaptic function.

Original languageEnglish
Title of host publicationBIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing
Pages48-53
Number of pages6
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2013 - Barcelona, Spain
Duration: 11 Feb 201314 Feb 2013

Other

OtherInternational Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2013
CountrySpain
CityBarcelona
Period11/2/1314/2/13

Fingerprint

Plasticity
Identification (control systems)
Reverse engineering
Neurons
Brain

Keywords

  • Discrete-time transfer function models
  • Dominant sub-processes
  • Long term depression
  • Synaptic plasticity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Signal Processing

Cite this

Tambuyzer, T., Ahmed, T., Taylor, C. J., Berckmans, D., Balschun, D., & Aerts, J. M. (2013). Dynamic data-based modelling of synaptic plasticity: mGluR-dependent long-term depression. In BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (pp. 48-53)

Dynamic data-based modelling of synaptic plasticity : mGluR-dependent long-term depression. / Tambuyzer, T.; Ahmed, Tariq; Taylor, C. J.; Berckmans, D.; Balschun, D.; Aerts, J. M.

BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2013. p. 48-53.

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

Tambuyzer, T, Ahmed, T, Taylor, CJ, Berckmans, D, Balschun, D & Aerts, JM 2013, Dynamic data-based modelling of synaptic plasticity: mGluR-dependent long-term depression. in BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. pp. 48-53, International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2013, Barcelona, Spain, 11/2/13.
Tambuyzer T, Ahmed T, Taylor CJ, Berckmans D, Balschun D, Aerts JM. Dynamic data-based modelling of synaptic plasticity: mGluR-dependent long-term depression. In BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2013. p. 48-53
Tambuyzer, T. ; Ahmed, Tariq ; Taylor, C. J. ; Berckmans, D. ; Balschun, D. ; Aerts, J. M. / Dynamic data-based modelling of synaptic plasticity : mGluR-dependent long-term depression. BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2013. pp. 48-53
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