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1 Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Physics, Humboldt-Universitat zu Berlin, Berlin, Germany
2 Department of Physics, Humboldt-Universitat zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
3 Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Biology, Humboldt-Universitat zu Berlin, Berlin, Germany; Department of Biology, Ludwig-Maximilians-Universitat Munich, Munich, Germany
4 Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Biology, Humboldt-Universitat zu Berlin, Berlin, Germany
5 Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, Scotland, United Kingdom; Center for Neuroscience Research, University of Edinburgh, Edinburgh, Scotland, United Kingdom
* To whom correspondence should be addressed. E-mail: ierchova{at}inf.ed.ac.uk.
Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. As subthreshold resonances are known to infuence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated resonant as well as nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: (i) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering, (ii) spike-induced dynamics infuence the fne structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher fring rates (3 Hz). Both mechanisms are likely to account for the specifc discharge characteristics of various cell types.
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