D

Keller, K.; Rimpler, H.; Schneider, Georg; Aye, R. D.; Bauer, Irmgard; Baumann, Thomas W.; Beck, M.; Beil, A.; Blascheck, W.; Brand, N.; Brenneisen, Rudolf; Burger, A.; Carle, Reinhold; Chaurasia, N.; Diettrich, B.; Eilert, U.; Ferstl, W.; Fett, Monika; Franck, G.; Frank, B.; Gehrmann, B.; Gorecki, P.; Gracza, L.; Greiner, Sibylle; Grubert, M. W.; Hampe, S.; Hänsel, Rudolf; Harnischfeger, G.; Henkler, G.; Hensel, Andreas; Heubl, Günther; Hiermann, A.; Hiller, K.; Hoffmann-Bohm, K.; Hohmann, B.; Hölscher, U.; Horz, K. H.; Isaac, O.; Junior, Peter; Jüptner, J.; Jurenitsch, J.; Kampfer, M.; Kartnig, Theodor; Khaliefi, F.; Kletter, C.; Kober, M.; Koch, H.; Koehler, H.; Kopp, Brigitte; Kraus, J.; Kraus, Ludwig; Kreis, Wolfgang; Langhammer, L.; Liersch, R.; Lindequist, Ulrike; Loew, D.; Luckner, Martin; Mathys, K.; Merfort, Irmgard; Miething, H.; Moeck, S.; Mundt, S.; Paper, D.; Pilgrim, H.; Proksch, Peter; Ratka, O.; Rauwald, Hans W.; Reichling, J.; Schennen, C.; Schilz, W.; Schleinitz, H.; Schmidt, Sandra; Schneider, K.; Scholz, Eberhard; Schöpke, T.; Schwarze, H.; Schweins, S.; Schwell, B.; Seitz, R.; Siegers, C.‐P.; Simon, P.; Sonnenborn, U.; Ssymank, V.; Staesche, K.; Stahl‐Biskup, E.; Steuding, W.; Stuppner, Hermann; Teuscher, E.; Thober-Miething, H.; Uehleke, B.; Veit, Markus; Warncke, D.; Wink, Michaël; Wissinger-Gräfenhahn, U.; Zepernick, B.

doi:10.1007/978-3-642-58087-1_4

Cited by 1 publication

(1 citation statement)

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“…If the induced events e i,m are distributed in event space E i less locally, with the e i,m located on different, possibly non-adjacent patches in E i , we systematically collect only those events that are located close to selected reference events e i,r [29]. To check performance, we systematically varied how irregular spike timing patterns are by varying the overall coupling strength by a factor of 30, effectively interpolating between regimes close to regular dynamics (locking) [30] for small coupling and close to irregular balanced states [12,[31][32][33] for large coupling. If sufficiently many events (ISI-CSI combinations) that are close in event space occur in the network's spike timing patterns, sampling the events in some local region (or, alternatively, local regions) in event space may be compensated by longer recording times or collecting several patches of events.…”

Section: Mapping Timing Patterns To Physical Connections?mentioning

confidence: 99%

Inferring Network Connectivity from Event Timing Patterns

2018

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Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution, although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their intrinsic collective dynamics generate. Representing the patterns of event timings in an event space spanned by interevent and cross-event intervals, we reveal which other units directly influence the interevent times of any given unit. For illustration, we linearize an event-space mapping constructed from the spiking patterns in model neural circuits to reveal the presence or absence of synapses between any pair of neurons, as well as whether the coupling acts in an inhibiting or activating (excitatory) manner. The proposed model-independent reconstruction theory is scalable to larger networks and may thus play an important role in the reconstruction of networks from biology to social science and engineering.

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