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Biological Cybernetics

, Volume 89, Issue 5, pp 363–370 | Cite as

Gain adjustment of inhibitory synapses in the auditory system

  • Vibhakar C. Kotak
  • Dan H. SanesEmail author
Article

Abstract.

A group of central auditory neurons residing in the lateral superior olivary nucleus (LSO) responds selectively to interaural level differences and may contribute to sound localization. In this simple circuit, ipsilateral sound increases firing of LSO neurons, whereas contralateral sound inhibits the firing rate via activation of the medial nucleus of the trapezoid body (MNTB). During development, individual MNTB fibers arborize within the LSO, but they undergo a restriction of their boutons that ultimately leads to mature topography. A critical issue is whether a distinct form of inhibitory synaptic plasticity contributes to MNTB synapse elimination within LSO. Whole-cell recording from LSO neurons in brain slices from developing gerbils show robust long-term depression (LTD) of the MNTB-evoked IPSP/Cs when the MNTB was activated at a low frequency (1 Hz). These inhibitory synapses also display mixed GABA/glycinergic transmission during development, as assessed physiologically and immunohistochemically (Kotak et al. 1998). While either glycine or GABAA receptors could independently display inhibitory LTD, focal delivery of GABA, but not glycine, at the postsynaptic-locus induces depression. Furthermore, the GABAB receptor antagonist, SCH-50911, prevents GABA or synaptically induced depression. Preliminary evidence also indicated strengthening of inhibitory transmission (LTP) by a distinct pattern of inhibitory activity. These data support the idea that GABA is crucial for the expression inhibitory LTD and that this plasticity may underlie the early refinement of inhibitory synaptic connections in the LSO.

Keywords

GABAA Receptor GABAB Receptor Inhibitory Synapse Auditory Neuron Interaural Level Difference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgments.

This work was supported by NIH DC00540 (DHS).

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  1. 1.Center for Neural ScienceNew York UniversityNew YorkUSA

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