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Metaplasticity at identified inhibitory synapses in Aplysia

Author

Listed:
  • Thomas M. Fischer

    (Yale University)

  • Diana E. J. Blazis

    (Yale University
    University of Massachusetts)

  • Naomi A. Priver

    (Yale University)

  • Thomas J. Carew

    (Yale University)

Abstract

Synaptic plasticity is an important feature of neural networks involved in the encoding of information. In the analysis of long-term potentiation and long-term depression, several examples have emerged in which this plasticity is itself modulated1,2,3. This higher-order form of plasticity has been referred to as ‘metaplasticity’4, a modification of synapses reflected as a change in the ability to induce or maintain plasticity. These observations raise the question of the possible advantage of regulating the intrinsic plastic properties of a synapse. The neural circuit mediating the siphon withdrawal reflex in Aplysia provides a useful network in which to examine this question directly. Inhibitory synapses in this circuit (from L30 neurons) exhibit a variety of forms of activity-dependent short-term synaptic enhancement which contribute to dynamic gain control in the siphon withdrawal reflex5,6,7,8,9. Here we report that tail shock, an extrinsic modulatory input of known behavioural relevance, induces differential metaplasticity at this synapse, attenuating its ability to exhibit short-term synaptic enhancement after presynaptic activation (augmentation and post-tetanic potentiation), while leaving intact its capacity for enhancement during activation. This attenuation of inhibition at the synaptic level seems to mediate comparable attenuation of inhibitory modulation at both network and behavioural levels.

Suggested Citation

  • Thomas M. Fischer & Diana E. J. Blazis & Naomi A. Priver & Thomas J. Carew, 1997. "Metaplasticity at identified inhibitory synapses in Aplysia," Nature, Nature, vol. 389(6653), pages 860-865, October.
  • Handle: RePEc:nat:nature:v:389:y:1997:i:6653:d:10.1038_39892
    DOI: 10.1038/39892
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    Cited by:

    1. Peng, Jiayi & Beggs, John M., 2013. "Attaining and maintaining criticality in a neuronal network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1611-1620.

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