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Cited by 593Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchAdvanced Memory and Neural Computing

Phenomenological models of synaptic plasticity based on spike timing

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Morrison, Abigail, Diesmann, Markus, Gerstner, Wulfram·2008·Biological Cybernetics
DOI 10.1007/s00422-008-0233-1PMC PMC2799003
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Abstract

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.

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  • If the contribution of CaMKII to synaptic transmission were a net gain that simply adds onto the preexisting excitatory drive of the cell, the system would be unstable and quickly saturate.

    “If this were the case, it has been argued that the system would be unstable and quickly saturate (366–371).”

    From Synaptic memory and CaMKII (2023) · cites this paper as [371]

    Section 4.1, Pair-based STDP rules (page unavailable), says that an additive plasticity model has an unstable fixed point and that individual synaptic weights move to their upper or lower bounds. It does not make this argument about CaMKII or say saturation is quick. It also says the neuron's mean firing rate is stabilized, so instability of individual weights should not be described as instability of the whole system. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The existence of LTD avoids the problem of saturation, which could be essential for a mnemonic device.

    “The existence of LTD avoids the problem of saturation, which could be essential for a mnemonic device (366–371).”

    From Synaptic memory and CaMKII (2023) · cites this paper as [371]

    Section 4.4.3 discusses saturation of LTP/LTD after repeated spike pairings, and Section 4.4.1 discusses the need for synaptic stability to retain memories. Neither passage says that LTD prevents saturation in a memory device. Searches for LTD preventing saturation and for depression preventing synaptic-weight saturation found no passage making that connection. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

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Authors
Morrison, A., Diesmann, M., & Gerstner, W.
Affiliations
  • RIKEN Center for Brain Science (JP)
  • University of Freiburg (DE)
  • École Polytechnique Fédérale de Lausanne (CH)
Corresponding author
Gerstner, Wulfram
ORCID
Venue
Biological Cybernetics
Publisher
Springer Science and Business Media LLC
Published
2008-05-20
Volume
98
Issue
6
Pages
459-478
ISSN
  • 0340-1200 (ISSN-L)
  • 1432-0770
DOI
10.1007/s00422-008-0233-1
PMC
PMC2799003
OpenAlex
W2151542182
Cited by
593
References
9
Citations by year
2026: 36 · 2025: 38 · 2024: 37 · 2023: 25 · 2022: 46 · 2021: 40 · 2020: 30 · 2019: 22 · 2018: 40 · 2017: 31 · 2016: 41 · 2015: 27 · 2014: 28 · 2013: 26 · 2012: 31
FWCI
2.25
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Biological Cybernetics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • reroDoc Digital Library (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Funders
Bundesministerium für Bildung und Forschung (01GQ0420)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Keywords
synaptic plasticity, spike-timing-dependent plasticity, short-term plasticity, long-term plasticity, phenomenological plasticity model, integrate-and-fire neurons, spike timing, membrane potential, supervised learning, reinforcement learning, learning and memory, large-scale network simulation
MeSH terms
Action Potentials, Animals, Models, Neurological, Neuronal Plasticity, Neurons, Synapses, Time Factors

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