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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Editorial: State-dependent brain computation</title><author><name sortKey="Ritter, Petra" sort="Ritter, Petra" uniqKey="Ritter P" first="Petra" last="Ritter">Petra Ritter</name><affiliation><nlm:aff id="aff1"><institution>Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences</institution><country>Leipzig, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>Deparment of Neurology, Charité - University Medicine</institution><country>Berlin, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff3"><institution>Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience</institution><country>Berlin, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution>Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University</institution><country>Berlin, Germany</country></nlm:aff></affiliation></author><author><name sortKey="Jirsa, Viktor K" sort="Jirsa, Viktor K" uniqKey="Jirsa V" first="Viktor K." last="Jirsa">Viktor K. Jirsa</name><affiliation><nlm:aff id="aff5"><institution>Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine</institution><country>Marseille, France</country></nlm:aff></affiliation></author><author><name sortKey="Mcintosh, Anthony R" sort="Mcintosh, Anthony R" uniqKey="Mcintosh A" first="Anthony R." last="Mcintosh">Anthony R. Mcintosh</name><affiliation><nlm:aff id="aff6"><institution>Rotman Research Institute of Baycrest Centre, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation></author><author><name sortKey="Breakspear, Michael" sort="Breakspear, Michael" uniqKey="Breakspear M" first="Michael" last="Breakspear">Michael Breakspear</name><affiliation><nlm:aff id="aff7"><institution>Systems Neuroscience Group, QIMR Berghofer</institution><country>Brisbane, QLD, Australia</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff8"><institution>The Royal Brisbane and Woman's Hospital</institution><country>Brisbane, QLD, Australia</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26157384</idno><idno type="pmc">4477138</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4477138</idno><idno type="RBID">PMC:4477138</idno><idno type="doi">10.3389/fncom.2015.00077</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000160</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000160</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Editorial: State-dependent brain computation</title><author><name sortKey="Ritter, Petra" sort="Ritter, Petra" uniqKey="Ritter P" first="Petra" last="Ritter">Petra Ritter</name><affiliation><nlm:aff id="aff1"><institution>Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences</institution><country>Leipzig, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution>Deparment of Neurology, Charité - University Medicine</institution><country>Berlin, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff3"><institution>Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience</institution><country>Berlin, Germany</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution>Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University</institution><country>Berlin, Germany</country></nlm:aff></affiliation></author><author><name sortKey="Jirsa, Viktor K" sort="Jirsa, Viktor K" uniqKey="Jirsa V" first="Viktor K." last="Jirsa">Viktor K. Jirsa</name><affiliation><nlm:aff id="aff5"><institution>Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine</institution><country>Marseille, France</country></nlm:aff></affiliation></author><author><name sortKey="Mcintosh, Anthony R" sort="Mcintosh, Anthony R" uniqKey="Mcintosh A" first="Anthony R." last="Mcintosh">Anthony R. Mcintosh</name><affiliation><nlm:aff id="aff6"><institution>Rotman Research Institute of Baycrest Centre, University of Toronto</institution><country>Toronto, ON, Canada</country></nlm:aff></affiliation></author><author><name sortKey="Breakspear, Michael" sort="Breakspear, Michael" uniqKey="Breakspear M" first="Michael" last="Breakspear">Michael Breakspear</name><affiliation><nlm:aff id="aff7"><institution>Systems Neuroscience Group, QIMR Berghofer</institution><country>Brisbane, QLD, Australia</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff8"><institution>The Royal Brisbane and Woman's Hospital</institution><country>Brisbane, QLD, Australia</country></nlm:aff></affiliation></author></analytic><series><title level="j">Frontiers in Computational Neuroscience</title><idno type="eISSN">1662-5188</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Banerjee, A" uniqKey="Banerjee A">A. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Comput Neurosci</journal-id><journal-id journal-id-type="iso-abbrev">Front Comput Neurosci</journal-id><journal-id journal-id-type="publisher-id">Front. Comput. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Computational Neuroscience</journal-title></journal-title-group><issn pub-type="epub">1662-5188</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26157384</article-id><article-id pub-id-type="pmc">4477138</article-id><article-id pub-id-type="doi">10.3389/fncom.2015.00077</article-id><article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject><subj-group><subject>Editorial</subject></subj-group></subj-group></article-categories><title-group><article-title>Editorial: State-dependent brain computation</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Ritter</surname><given-names>Petra</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/4729/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Jirsa</surname><given-names>Viktor K.</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/4334/overview"></uri></contrib><contrib contrib-type="author"><name><surname>McIntosh</surname><given-names>Anthony R.</given-names></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/4730/overview"></uri></contrib><contrib contrib-type="author"><name><surname>Breakspear</surname><given-names>Michael</given-names></name><xref ref-type="aff" rid="aff7"><sup>7</sup></xref><xref ref-type="aff" rid="aff8"><sup>8</sup></xref><uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/20295/overview"></uri></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences</institution><country>Leipzig, Germany</country></aff><aff id="aff2"><sup>2</sup><institution>Deparment of Neurology, Charité - University Medicine</institution><country>Berlin, Germany</country></aff><aff id="aff3"><sup>3</sup><institution>Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience</institution><country>Berlin, Germany</country></aff><aff id="aff4"><sup>4</sup><institution>Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University</institution><country>Berlin, Germany</country></aff><aff id="aff5"><sup>5</sup><institution>Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine</institution><country>Marseille, France</country></aff><aff id="aff6"><sup>6</sup><institution>Rotman Research Institute of Baycrest Centre, University of Toronto</institution><country>Toronto, ON, Canada</country></aff><aff id="aff7"><sup>7</sup><institution>Systems Neuroscience Group, QIMR Berghofer</institution><country>Brisbane, QLD, Australia</country></aff><aff id="aff8"><sup>8</sup><institution>The Royal Brisbane and Woman's Hospital</institution><country>Brisbane, QLD, Australia</country></aff><author-notes><fn fn-type="edited-by"><p>Edited and reviewed by: Si Wu, Beijing Normal University, China</p></fn><corresp id="fn001">*Correspondence: Petra Ritter, <email xlink:type="simple">petra.ritter@charite.de</email></corresp></author-notes><pub-date pub-type="epub"><day>23</day><month>6</month><year>2015</year></pub-date><pub-date pub-type="collection"><year>2015</year></pub-date><volume>9</volume><elocation-id>77</elocation-id><history><date date-type="received"><day>30</day><month>5</month><year>2015</year></date><date date-type="accepted"><day>10</day><month>6</month><year>2015</year></date></history><permissions><copyright-statement>Copyright © 2015 Ritter, Jirsa, McIntosh and Breakspear.</copyright-statement><copyright-year>2015</copyright-year><copyright-holder>Ritter, Jirsa, McIntosh and Breakspear</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><kwd-group><kwd>state-dependence</kwd><kwd>computational neuroscience</kwd><kwd>brain scales</kwd><kwd>computational modeling</kwd><kwd>empirical research</kwd></kwd-group><funding-group><award-group><funding-source id="cn001">German Ministry of Education and Research</funding-source><award-id rid="cn001">Bernstein Focus State Dependencies of Learning 01GQ0971</award-id></award-group><award-group><funding-source id="cn002">James S. McDonnell Foundation</funding-source><award-id rid="cn002">Brain Network Recovery Group JSMF22002082</award-id></award-group></funding-group><counts><fig-count count="0"></fig-count><table-count count="1"></table-count><equation-count count="0"></equation-count><ref-count count="18"></ref-count><page-count count="4"></page-count><word-count count="2001"></word-count></counts></article-meta></front><body><p>The brain is a self-organizing system, which has evolved such that neuronal responses and related behavior are continuously adapted with respect to the external and internal context. This powerful capability is achieved through the modulation of neuronal interactions depending on the history of previously processed information. In particular, the brain updates its connections as it learns successful versus unsuccessful strategies. The resulting connectivity changes, together with stochastic processes (i.e., noise) influence ongoing neuronal dynamics. The role of such state-dependent fluctuations may be one of the fundamental computational properties of the brain, being pervasively present in human behavior and leaving a distinctive fingerprint in neuroscience data. This development is captured by the present Frontiers Research Topic, “State-Dependent Brain Computation.”</p><p>The Research Topic provides an account of prevailing concepts and theories plus recent advances on the role of ongoing brain dynamics—reflecting experiences, global brain states, context and noise—for task-related information processing. Works from the conceptual, experimental and computational-modeling domains are show-cased, focusing on the following two issues: (1) Generative mechanisms of ongoing neuronal dynamics, and (2) Principles of interaction between ongoing dynamics and perceptual or motor processes.</p><p>A wide range of spatial and temporal scales encountered in brain dynamics are covered, i.e., from microscopic molecular to macroscopic population dynamics and from fast processes evolving within milliseconds to slow ones taking hours or longer (Table <xref ref-type="table" rid="T1">1</xref>). An overview article about state-depended learning exemplifies the need for integration of different scales of processing (Ritter et al., <xref rid="B15" ref-type="bibr">2015</xref>). The role of ongoing alpha oscillations at the microscopic and macroscopic scale for learning is illuminated in Sigala et al. (<xref rid="B18" ref-type="bibr">2014</xref>). In this study, the authors present empirical data along with computational models that seek to unveil the underlying principles how oscillations interact with synaptic plasticity. EEG dynamics are also explored in Betzel et al. (<xref rid="B2" ref-type="bibr">2012</xref>) where the authors report fast synchronization dynamics—in the range of tens to hundreds of milliseconds—iterating amongst a small set of core networks in the resting brain. The authors suggest that these dynamics may be the neural correlate of resting state BOLD fluctuations. The ability of stochastic dynamic causal modeling (DCM) for fMRI—a neural field formulation of cortical activity—is probed in Daunizeau et al. (<xref rid="B3" ref-type="bibr">2012</xref>) where EEG spectral changes are predicted from BOLD signal Fast and high spatial frequency modes as represented in EEG are enslaved by slow and slow spatial frequency modes predominant in fMRI signals. Using an Ising spin model (Deco et al., <xref rid="B4" ref-type="bibr">2012</xref>) demonstrate that the dynamic repertoire of the brain, i.e., different spatio-temporal patterns of functional connectivity, emerges naturally from the neuroanatomical connectivity. It is hypothesized that the scale-free neuroanatomical architecture maximizes the dynamic repertoire and its accessibility in the human brain. Critical slowing caused by dynamical instabilities that are triggered by perception is proposed to enable the brain to process sensory perturbations (Friston et al., <xref rid="B6" ref-type="bibr">2012</xref>). Neuronal oscillator models with surround inhibition were shown to generate bistable spatial patterns of activity (Heitmann et al., <xref rid="B7" ref-type="bibr">2012</xref>) and indicate that state-dependent computations may facilitate rapid switching between motor states, potentially accommodating high speed rather than precision responses. The cross-frequency coupling present in empirical EEG was systematically simulated in a full human brain network model of coupled neuronal oscillators (Jirsa and Muller, <xref rid="B9" ref-type="bibr">2013</xref>) for eyes-open and eyes-closed states of rest condition and the theoretical implications for state-dependent processing discussed. Distinct brain states linked to motor and perceptual visuo-spatial working memory and accompanying specific mental processes are characterized as spatio-temporal functional connectivity patterns in EEG (Protopapa et al., <xref rid="B13" ref-type="bibr">2014</xref>).</p><table-wrap id="T1" position="float"><label>Table 1</label><caption><p><bold>Different facets of state-dependent brain computation are illuminated in the present Research Topic</bold>.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"><bold>Scale</bold></th><th align="left" rowspan="1" colspan="1"><bold>Article</bold></th><th align="left" rowspan="1" colspan="1"><bold>Animal</bold></th><th align="left" rowspan="1" colspan="1"><bold>Function</bold></th><th align="left" rowspan="1" colspan="1"><bold>States</bold></th><th align="left" rowspan="1" colspan="1"><bold>Empirical</bold></th><th align="left" rowspan="1" colspan="1"><bold>Model</bold></th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Micro</td><td align="left" rowspan="1" colspan="1">Banerjee et al., <xref rid="B1" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Macaque</td><td align="left" rowspan="1" colspan="1">Arm movement/reaching task</td><td align="left" rowspan="1" colspan="1">LFP</td><td align="left" rowspan="1" colspan="1">Spike trains, LFPs</td><td align="left" rowspan="1" colspan="1">Parametric model</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Fernandez-Ruiz and Herreras, <xref rid="B5" ref-type="bibr">2013</xref></td><td align="left" rowspan="1" colspan="1">Rat</td><td align="left" rowspan="1" colspan="1">Rest, stimulation</td><td align="left" rowspan="1" colspan="1">LFP</td><td align="left" rowspan="1" colspan="1">LFP</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Humble et al., <xref rid="B8" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Memory</td><td align="left" rowspan="1" colspan="1">Spike trains</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Spiking neuron models and STDP</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Mark and Tsodyks, <xref rid="B10" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Rat</td><td align="left" rowspan="1" colspan="1">Spontaneous, sensory stimulations</td><td align="left" rowspan="1" colspan="1">Different synchrony levels</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Wilson-Cowan rate model,<break></break>IF-neurons,<break></break>Realistic neuronal network with clustered architecture</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Palma et al., <xref rid="B12" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Memory</td><td align="left" rowspan="1" colspan="1">Neuromodulation</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Spiking recurrent networks</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Quilichini and Bernard, <xref rid="B14" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Rat</td><td align="left" rowspan="1" colspan="1">T-maze</td><td align="left" rowspan="1" colspan="1">Neuromodulators</td><td align="left" rowspan="1" colspan="1">Firing pattern</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">Bridging micro to macro</td><td align="left" rowspan="1" colspan="1">Ritter et al., <xref rid="B15" ref-type="bibr">2015</xref></td><td align="left" rowspan="1" colspan="1">Human, macaque, rat, <italic>in vitro</italic></td><td align="left" rowspan="1" colspan="1">Learning, rest</td><td align="left" rowspan="1" colspan="1">Oscillatory<break></break>LFP<break></break>EEG<break></break>BOLD</td><td align="left" rowspan="1" colspan="1">Yes</td><td align="left" rowspan="1" colspan="1">Yes</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Sigala et al., <xref rid="B18" ref-type="bibr">2014</xref></td><td align="left" rowspan="1" colspan="1">Human/young adults</td><td align="left" rowspan="1" colspan="1">Learning, rest</td><td align="left" rowspan="1" colspan="1">Oscillatory<break></break>LFP<break></break>EEG<break></break>BOLD</td><td align="left" rowspan="1" colspan="1">Yes</td><td align="left" rowspan="1" colspan="1">Yes</td></tr><tr><td align="left" rowspan="1" colspan="1">Macro</td><td align="left" rowspan="1" colspan="1">Betzel et al., <xref rid="B2" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Rest</td><td align="left" rowspan="1" colspan="1">EEG, 10–100 ms</td><td align="left" rowspan="1" colspan="1">EEG</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Daunizeau et al., <xref rid="B3" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human/patients with epilepsy</td><td align="left" rowspan="1" colspan="1">Rest, interictal activity</td><td align="left" rowspan="1" colspan="1">EEG<break></break>BOLD</td><td align="left" rowspan="1" colspan="1">EEG, BOLD</td><td align="left" rowspan="1" colspan="1">Neuronal field model/stochastic<break></break>DCM, Heuristic model</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Deco et al., <xref rid="B4" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Rest</td><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">BOLD</td><td align="left" rowspan="1" colspan="1">Ising spin model</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Heitmann et al., <xref rid="B7" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Motor</td><td align="left" rowspan="1" colspan="1">LFP/EEG</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Neuronal oscillator model</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Jirsa and Muller, <xref rid="B9" ref-type="bibr">2013</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Rest, eyes-closed, eyes-open</td><td align="left" rowspan="1" colspan="1">EEG</td><td align="left" rowspan="1" colspan="1">EEG</td><td align="left" rowspan="1" colspan="1">generic oscillator equations derived from coupled full brain network</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Miller et al., <xref rid="B11" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Rest and task/stimulation</td><td align="left" rowspan="1" colspan="1">Band-limited EEG rhythms amplitude modulation</td><td align="left" rowspan="1" colspan="1">EEG</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">Protopapa et al., <xref rid="B13" ref-type="bibr">2014</xref></td><td align="left" rowspan="1" colspan="1">Human</td><td align="left" rowspan="1" colspan="1">Motor and visuo-spatial working memory</td><td align="left" rowspan="1" colspan="1">EEG functional connectivity</td><td align="left" rowspan="1" colspan="1">EEG</td><td align="left" rowspan="1" colspan="1">–</td></tr><tr><td align="left" rowspan="1" colspan="1">Not specified</td><td align="left" rowspan="1" colspan="1">Friston et al., <xref rid="B6" ref-type="bibr">2012</xref></td><td align="left" rowspan="1" colspan="1">Human, bird</td><td align="left" rowspan="1" colspan="1">Sensory stimulation, bird song</td><td align="left" rowspan="1" colspan="1">Behavioral, simulated neuronal dynamics</td><td align="left" rowspan="1" colspan="1">–</td><td align="left" rowspan="1" colspan="1">Generalized Bayesian filtering, generic generative model</td></tr></tbody></table></table-wrap><p>On the microscopic scale, spike trains and local field potentials (LFP) dynamics are set in relation (Banerjee et al., <xref rid="B1" ref-type="bibr">2012</xref>) using parametric models with the goal to decode those signals and infer related behaviors. In Fernandez-Ruiz and Herreras (<xref rid="B5" ref-type="bibr">2013</xref>) it is pointed out that LFP's are highly variable over time and have flexible spectrums, i.e., the notion of periodic oscillations commonly used to describe brain activity is questioned. These authors propose a method to de-mix LFPs of different sources to determine the true degree of periodicity—a prerequisite for a mechanistic understanding of information transfer in the brain. In a spiking neuronal model with STDP (Humble et al., <xref rid="B8" ref-type="bibr">2012</xref>) demonstrate that simple networks of laterally connected excitatory neurons can self-organize into spatio-temporal pattern recognizers. The potential for representations of more complex nested patterns which implies stronger computational memory capabilities is raised. The flow of information depends on the degree of network synchrony (Mark and Tsodyks, <xref rid="B10" ref-type="bibr">2012</xref>) and an intermediate degree of synchrony is most beneficial for information transfer. The question whether rhythmic entrainment represents a general mechanism of computation in the brain is raised and ways are pointed out how to address this question through empirical work in the future (Miller et al., <xref rid="B11" ref-type="bibr">2012</xref>). The theoretical impact of neuromodulation on memory formation in spiking recurrent cortical networks is systematically evaluated (Palma et al., <xref rid="B12" ref-type="bibr">2012</xref>). In a perspective article, a systematic account is provided how intrinsic properties of neurons and neuromodulation relates to firing patterns, functional correlations and behavior in rats (Quilichini and Bernard, <xref rid="B14" ref-type="bibr">2012</xref>).</p><p>The degree of abstraction in the modeling work presented in this Research Topic varies tremendously, ranging from simplified but biophysically plausible network models to highly detailed neuron models. By placing the different mathematical and empirical aspects in this mutual context, this Research Topic aims to elucidate the principle mechanisms of state-dependent neuronal processing. Developing a framework to link the multiple principles together is arguably the most pressing challenge. With The Virtual Brain (thevirtualbrain.org) simulation framework (Ritter et al., <xref rid="B16" ref-type="bibr">2013</xref>; Sanz Leon et al., <xref rid="B17" ref-type="bibr">2013</xref>) we hope to contribute to this endeavor by enabling researchers to use multiple modeling approaches in a unified framework ensuring reproducibility and comparability of results.</p><sec><title>Conflict of interest statement</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>The authors acknowledge the support of the German Ministry of Education and Research (Bernstein Focus State Dependencies of Learning 01GQ0971) to PR, the James S. 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