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Conservation of total synaptic weight through balanced synaptic depression and potentiation

Author

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  • Sébastien Royer

    (Rutgers State University
    Rutgers State University)

  • Denis Paré

    (Rutgers State University
    Rutgers State University)

Abstract

Memory is believed to depend on activity-dependent changes in the strength of synapses1. In part, this view is based on evidence that the efficacy of synapses can be enhanced or depressed depending on the timing of pre- and postsynaptic activity2,3,4,5. However, when such plastic synapses are incorporated into neural network models, stability problems may develop because the potentiation or depression of synapses increases the likelihood that they will be further strengthened or weakened6. Here we report biological evidence for a homeostatic mechanism that reconciles the apparently opposite requirements of plasticity and stability. We show that, in intercalated neurons of the amygdala, activity-dependent potentiation or depression of particular glutamatergic inputs leads to opposite changes in the strength of inputs ending at other dendritic sites. As a result, little change in total synaptic weight occurs, even though the relative strength of inputs is modified. Furthermore, hetero- but not homosynaptic alterations are blocked by intracellular dialysis of drugs that prevent Ca2+ release from intracellular stores. Thus, in intercalated neurons at least, inverse heterosynaptic plasticity tends to compensate for homosynaptic long-term potentiation and depression, thus stabilizing total synaptic weight.

Suggested Citation

  • Sébastien Royer & Denis Paré, 2003. "Conservation of total synaptic weight through balanced synaptic depression and potentiation," Nature, Nature, vol. 422(6931), pages 518-522, April.
  • Handle: RePEc:nat:nature:v:422:y:2003:i:6931:d:10.1038_nature01530
    DOI: 10.1038/nature01530
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    Cited by:

    1. Giorgia Dellaferrera & Stanisław Woźniak & Giacomo Indiveri & Angeliki Pantazi & Evangelos Eleftheriou, 2022. "Introducing principles of synaptic integration in the optimization of deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Thomas E. Chater & Maximilian F. Eggl & Yukiko Goda & Tatjana Tchumatchenko, 2024. "Competitive processes shape multi-synapse plasticity along dendritic segments," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Hang Zhou & Guo-Qiang Bi & Guosong Liu, 2024. "Intracellular magnesium optimizes transmission efficiency and plasticity of hippocampal synapses by reconfiguring their connectivity," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    4. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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