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Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators

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Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. A link to any extended data will be provided when the final version is posted online. Research Articles: Systems/Circuits Hippocampal ripple oscillations and inhibition-first network models: Frequency dynamics and response to GABA modulators. José R. Donoso1,2, Dietmar Schmitz2,3,4,5,6, Nikolaus Maier3 and Richard Kempter1,2,6 1Humboldt-Universität zu Berlin, Department of Biology, Institute for Theoretical Biology, Philippstr. 13, 10115 Berlin, Germany 2Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany 3Charité— Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health; Neuroscience Research Center, 10117 Berlin, Germany 4NeuroCure Cluster of Excellence, Charité— Universitätsmedizin Berlin, 10117 Berlin, Germany 5Deutsches Zentrum für Neurodegenerative Erkrankungen in der Helmholtz-Gemeinschaft, Charité— Universitätsmedizin Berlin, 10117 Berlin, Germany 6Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany DOI: 10.1523/JNEUROSCI.0188-17.2018 Received: 20 January 2017 Revised: 25 January 2018 Accepted: 5 February 2018 Published: 16 February 2018 Author contributions: J.R.D., D.S., N.M., and R.K. designed research; J.R.D., N.M., and R.K. performed research; J.R.D., N.M., and R.K. analyzed data; J.R.D. wrote the first draft of the paper; J.R.D., D.S., N.M., and R.K. edited the paper; J.R.D., D.S., N.M., and R.K. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. This work was supported by the Bundesministerium für Bildung und Forschung (Bernstein Center for Computational Neuroscience Berlin, grant 01GQ1001A; Bernstein Focus “Neuronal Foundations of Learning” grant 01GQ0972), the Deutsche Forschungsgemeinschaft (GRK1589, SFB958, KE788/3-1), the NeuroCure excellence cluster, and the Einstein Center for Neurosciences Berlin. We thank Roger D. Traub for valuable discussions, and Nikolai Chenkov, André Holzbecher, Eric Reifenstein, and Natalie Schieferstein for comments on the manuscript. Corresponding author: Richard Kempter Humboldt-Universität zu Berlin, Department of Biology, Institute for Theoretical Biology, Philippstr. 13, 10115 Berlin, Germany phone: +49-30-2093 98404 email: [email protected] Cite as: J. Neurosci ; 10.1523/JNEUROSCI.0188-17.2018 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2018 the authors Title:Hippocampal ripple oscillations and inhibition-first network models: Frequency dynamics and response to GABA modulators. Abbreviated title: Ripples in interneuron networks. Jose´ R. Donoso1,2, Dietmar Schmitz2,3,4,5,6, Nikolaus Maier3,*, and Richard Kempter1,2,6,* 1 Humboldt-Universitat¨ zu Berlin, Department of Biology, Institute for Theoretical Biology, Philippstr. 13, 10115 Berlin, Germany 2 Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, 10115 Berlin, Germany 3 Charite´ – Universitatsmedizin¨ Berlin, corporate member of Freie Universitat¨ Berlin, Humboldt-Universitat¨ zu Berlin, and Berlin Institute of Health; Neuroscience Research Center, 10117 Berlin, Germany 4 NeuroCure Cluster of Excellence, Charite´ – Universitatsmedizin¨ Berlin, 10117 Berlin, Germany 5 Deutsches Zentrum fur¨ Neurodegenerative Erkrankungen in der Helmholtz-Gemeinschaft, Charite´ – Universitatsmedizin¨ Berlin, 10117 Berlin, Germany 6 Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany * Co-last authors Corresponding author: Richard Kempter Humboldt-Universitat¨ zu Berlin, Department of Biology, Institute for Theoretical Biology, Philippstr. 13, 10115 Berlin, Germany phone: +49-30-2093 98404 email: [email protected] Number of pages: 52 (including the titlepage, not including figures) Number of figures: 9 Number of words in Abstract: 249 Number of words in Introduction: 646 Number of words in Discussion: 1448 Conflict of Interest: The authors declare no competing financial interests. Acknowledgements: This work was supported by the Bundesministerium fur¨ Bildung und Forschung (Bernstein Center for Computational Neuroscience Berlin, grant 01GQ1001A; Bernstein Focus “Neuronal Foundations of Learning” grant 01GQ0972), the Deutsche Forschungsgemeinschaft (GRK1589, SFB958, KE788/3-1), the NeuroCure excellence cluster, and the Einstein Center for Neurosciences Berlin. We thank Roger D. Traub for valuable discussions, and Nikolai Chenkov, Andre´ Holzbecher, Eric Reifenstein, and Natalie Schieferstein for comments on the manuscript. 1 1 Abstract 2 Hippocampal ripples are involved in memory consolidation, but the mechanisms underlying their generation 3 remain unclear. Models relying on interneuron networks in the CA1 region disagree on the predominant 4 source of excitation to interneurons: either ‘direct’, via the Schaffer collaterals that provide feedforward 5 input from CA3 to CA1, or ‘indirect’, via the local pyramidal cells in CA1, which are embedded in a 6 recurrent excitatory-inhibitory network. Here, we used physiologically constrained computational models 7 of basket-cell networks to investigate how they respond to different conditions of transient, noisy excitation. 8 We found that direct excitation of interneurons could evoke ripples (140–220 Hz) that exhibited intra- 9 ripple frequency accommodation (IFA) and were frequency-insensitive to GABA modulators, as previously 10 shown in in-vitro experiments. In addition, the indirect excitation of the basket-cell network enabled the 11 expression of IFA in the fast-gamma range (90–140 Hz), as in vivo. In our model, IFA results from a 12 hysteresis phenomenon in which the frequency responds differentially to the rising and descending phases 13 of the transient excitation. Such a phenomenon predicts a maximum oscillation frequency occurring several 14 milliseconds before the peak of excitation. We confirmed this prediction for ripples in brain slices from 15 male mice. These results suggest that ripple and fast-gamma episodes are produced by the same interneuron 16 network that is recruited via different excitatory input pathways, which could be supported by the previously 17 reported intralaminar connectivity bias between basket cells and functionally distinct subpopulations of 18 pyramidal cells in CA1. Taken together, our findings unify competing inhibition-first models of rhythm 19 generation in the hippocampus. 2 20 Significance Statement 21 The hippocampus is a part of the brain of humans and other mammals that is critical for the acquisi- 22 tion and consolidation of memories. During deep sleep and resting periods, the hippocampus generates 23 high-frequency (∼200 Hertz) oscillations called ripples, which are important for memory consolidation. 24 The mechanisms underlying ripple generation are not well understood. A prominent hypothesis holds that 25 the ripples are generated by local recurrent networks of inhibitory neurons. Using computational models 26 and experiments in brain slices from rodents, we show that the dynamics of interneuron networks clarify 27 several previously unexplained characteristics of ripple oscillations, which advances our understanding of 28 hippocampus-dependent memory consolidation. 3 29 Introduction 30 The hippocampal local field potential displays various oscillations that are associated to different behav- 31 ioral states (Buzsaki´ and Draguhn, 2004). Theta and gamma oscillations occur during locomotor activities 32 (Vanderwolf, 1969; Buzsaki´ et al., 1983; Bragin et al., 1995) at which sensory information is temporar- 33 ily stored in the hippocampus. Sharp wave-ripple complexes (SWRs) are prominent in periods of im- 34 mobility, consummatory behaviors, and slow-wave sleep in rodents (Buzsaki´ et al., 1983; Buzsaki,´ 1986; 35 Buzsaki´ et al., 1992; Wilson and McNaughton, 1994; Karlsson and Frank, 2009) and humans (Bragin et al., 36 1999; Axmacher et al., 2008). During SWRs, previously acquired information is replayed (Wilson and McNaughton, 37 1994; Nadasdy´ et al., 1999; Lee and Wilson, 2002; Foster and Wilson, 2006; Diba and Buzsaki,´ 2007; Gupta et al., 38 2010). SWRs were proposed to assist memory consolidation (Buzsaki,´ 1989; 1998; Siapas and Wilson, 39 1998; Girardeau and Zugaro, 2011). In support, the suppression of SWRs impairs spatial memory (Girardeau et al., 40 2009; Ego-Stengel and Wilson, 2010; Jadhav et al., 2012). However, mechanisms underlying the generation 41 of SWRs are not well understood (Buzsaki´ and Lopes da Silva, 2012). 42 SWRs consist of a fast (∼90–200 Hz) oscillation, the ripple, superimposed on a transient deflection of 43 the local field potential, the sharp wave (Buzsaki´ et al., 1992). The activity of various cell types is locked to 44 ripple oscillations but the pace-making mechanisms are unclear, although many in-vivo and in-vitro stud- 45 ies addressed this question (Ylinen et al., 1995; Draguhn et al., 1998; Nadasdy´ et al., 1999; Csicsvari et al., 46 2000; Klausberger et al., 2003; 2005; Maier et al., 2003; 2011; Lapray et al., 2012; Pangalos et al., 2013; 47 Hajos´ et al., 2013; Gan et al., 2017). Current hypotheses agree that the fast component emerges from in- 48 teractions between neurons, but models differ on where and how such oscillatory activity
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