Journal
JOURNAL OF NEUROSCIENCE
Volume 32, Issue 29, Pages 9931-9946Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.5446-11.2012
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Funding
- Natural Sciences and Engineering Research Council of Canada
- PGS-D award
- Queen Elizabeth II Graduate Scholarship in Science and Technology of Ontario
- University of Toronto Open Fellowships
- Lichtenberg Award, VW-Foundation
- BIOSS Centre for Biological Signalling Studies
- Excellence Initiative of the German Federal and State Governments [GSC-4]
- Canada Foundation for Innovation
- Compute Canada
- Government of Ontario
- Ontario Research Fund Research Excellence
- University of Toronto
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Slow population activities (SPAs) exist in the brain and have frequencies below similar to 5 Hz. Despite SPAs being prominent in several cortical areas and serving many putative functions, their mechanisms are not well understood. We studied a specific type of in vitro GABAergic, inhibition-based SPA exhibited by C57BL/6 murine hippocampus. We used a multipronged approach consisting of experiment, simulation, and mathematical analyses to uncover mechanisms responsible for hippocampal SPAs. Our results show that hippocampal SPAs are an emergent phenomenon in which the slowness of the network is due to interactions between synaptic and cellular characteristics of individual fast-spiking, inhibitory interneurons. Our simulations quantify characteristics underlying hippocampal SPAs. In particular, for hippocampal SPAs to occur, we predict that individual fast-spiking interneurons should have frequency-current (f-I) curves that exhibit a suitably sized kink where the slope of the curve decreases more abruptly in the gamma frequency range with increasing current. We also predict that these interneurons should be well connected with one another. Our mathematical analyses show that the combination of synaptic and intrinsic conditions, as predicted by our simulations, promotes network multistability. Population slow timescales occur when excitatory fluctuations drive the network between different stable network firing states. Since many of the parameters we use are extracted from experiments and subsequent measurements of experimental f-I curves of fast-spiking interneurons exhibit characteristics as predicted, we propose that our network models capture a fundamental operating mechanism in biological hippocampal networks.
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