ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2012
Inhibitory networks of fast-spiking interneurons generate slow population activities due to excitatory fluctuations and network multistability.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Site-dependent shaping of field potential waveforms.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Inhibitory Network Bistability Explains Increased Interneuronal Activity Prior to Seizure Onset.Frontiers in neural circuits · 2019Article
- Nanodiamonds-induced effects on neuronal firing of mouse hippocampal microcircuits.Scientific reports · 2018Article
- Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Interaction between synaptic inhibition and glial-potassium dynamics leads to diverse seizure transition modes in biophysical models of human focal seizures.Journal of computational neuroscience · 2016Article
- Regulation of Irregular Neuronal Firing by Autaptic Transmission.Scientific reports · 2016Article
- Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus.Journal of computational neuroscience · 2015Article
- Scale-free brain activity: past, present, and future.Trends in cognitive sciences · 2014Review
- Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context.F1000Research · 2014Article
- Network models predict that reduced excitatory fluctuations can give rise to hippocampal network hyper-excitability in MeCP2-null mice.PloS one · 2014Article
- Cytoarchitectonic and dynamic origins of giant positive local field potentials in the dentate gyrus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
- EXPONENTIAL TIME DIFFERENCING FOR HODGKIN-HUXLEY-LIKE ODES.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2013Article
- Moving beyond Type I and Type II neuron types.F1000Research · 2013Article
- Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms.Frontiers in computational neuroscience · 2013Article
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5 authors.
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Abstract
Slow population activities (SPAs) exist in the brain and have frequencies below ~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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