Rapid Signaling at Inhibitory Synapses in a Dentate Gyrus Interneuron Network

By M. Bartos, Imre Vida, M. Frotscher, Jörg R. P. Geiger, Peter Jonas. 2001

313 citations recorded by the source corpus.

Mutual synaptic interactions between GABAergic interneurons are thought to be of critical importance for the generation of network oscillations and for temporal encoding of information in the hippocampus. However, the functional properties of synaptic transmission between hippocampal interneurons are largely unknown. We have made paired recordings from basket cells (BCs) in the dentate gyrus of rat hippocampal slices, followed by correlated light and electron microscopical analysis. Unitary GABAAreceptor-mediated IPSCs at BC–BC synapses recorded at the soma showed a fast rise and decay, with a mean decay time constant of 2.5 ± 0.2 msec (32°C). Synaptic transmission at BC–BC synapses showed paired-pulse depression (PPD) (32 ± 5% for 10 msec interpulse intervals) and multiple-pulse depression during repetitive stimulation. Detailed passive cable model simulations based on somatodendritic morphology and localization of synaptic contacts further indicated that the conductance change at the postsynaptic site was even faster, decaying with a mean time constant of 1.8 ± 0.6 msec. Sequential triple recordings revealed that the decay time course of IPSCs at BC–BC synapses was approximately twofold faster than that at BC–granule cell synapses, whereas the extent of PPD was comparable. To examine the consequences of the fast postsynaptic conductance change for the generation of oscillatory activity, we developed a computational model of an interneuron network. The model showed robust oscillations at frequencies >60 Hz if the excitatory drive was sufficiently large. Thus the fast conductance change at interneuron–interneuron synapses may promote the generation of high-frequency oscillations observed in the dentate gyrusin vivo.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity (2016, methodology): “The peak conductance (gmax), rise and decay time constants, and the delay of various network connections were estimated from experimental data (Kneisler and Dingledine, 1995; Geiger et al., 1997; Bartos et al., 2001; Schmidt-Hieber et al., 2007; Larimer and Strowbridge, 2008; Schmidt-Hieber and Bisc”
Morpho-physiological Criteria Divide Dentatecc Gyrus Interneurons into Classes (2013, result): “These data are in line with previous electrophysiological evidences for a strong perisomatic inhibition of the large principal cell population (Cobb et al., 1995; Pouille and Scanziani, 2001; Bartos et al., 2001, 2007).”
Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks (2007, background): “Paired recording experiments in acute slices in vitro have shown that basket cells are highly interconnected in both the hippocampu”
What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. (2003, background): “There have been some analysis of how synchronization between coupled oscillators depends on the heterogeneity of neuronal properties (Bartos et al. 2001; Hansel and Mato 2003; Neltner et al. 2000; Tiesinga and Jose 2000; Wang and Buzsáki 1996; White et al. 1998) or the sparse random network…”
Synaptic Properties of SOM- and CCK-Expressing Cells in Dentate Gyrus Interneuron Networks (2014, methodology): “In contrast, depletion of the readily releasable pool (Stevens and Tsujimoto, 1995) is unlikely to contribute because of the ability of BC outputs to release transmitter at high frequency over prolonged periods (Kraushaar and Jonas, 2000; Bartos et al., 2001).”
Gamma and Theta Rhythms in Biophysical Models of Hippocampal Circuits (2010, methodology): “For these reasons, we have chosen to use parameters derived directly from the gamma oscillations rather than the technically more accurate measurements (Bartos et al., 2001, 2002) from situations that are less relevant to the data we model.”