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Publications of Computational Neurophysics

  • Hahne J., Dahmen D., Schuecker J., Frommer A., Bolten M., Helias M., Diesmann M. (2017). Integration of continuous-time dynamics in a spiking neural network simulator. Frontiers in Neuroinformatics 11:34. DOI: 10.3389/fninf.2017.00034.
  • Ippen T., Eppler JM., Plesser HE., Diesmann M. (2017). Constructing neuronal network models in massively parallel environments. Frontiers in Neuroinformatics 11:30. DOI: 10.3389/fninf.2017.00030.
  • Kass RE., Amari S., Arai K., Brown EN., Diekman CO., Diesmann M., Doiron B., Eden U., Fairhall A., Fiddyment GM., Fukai T., Grün S., Harrison MT., Helias M., Nakahara H., Teramae J., Thomas PJ., Reimers M., Rodu J., Rotstein HG., Shea-Brown E., Shimazaki H., Shinomoto S., Yu BM., Kramer MA. (2017). Computational neuroscience: mathematical and statistical perspectives. Annual Review of Statistics and Its Application 5 DOI: 10.1146/annurev-statistics-041715-033733.
  • Schmidt M., Bakker R., Hilgetag CC., Diesmann M., van Albada SJ. (2017). Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function 'advance online' DOI:10.1007/s00429-017-1554-4.
  • . DOI:10.1007/s00429-017-1554-4.
  • Schuecker J., Schmidt M., van Albada SJ., Diesmann M., Helias M. (2017). Fundamental activity constraints lead to specific interpretations of the connectome. PLOS Computational Biology 13:e1005179. DOI: 10.1371/journal.pcbi.1005179.
  • Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Ruebel O., Simon HD., Sommer FT., Prabhat (2016). High-performance computing in neuroscience for data-driven discovery, integration, and dissemination. Neuron 92:628–631. DOI: 10.1016/j.neuron.2016.10.035.
  • Bos H., Diesmann M., Helias M. (2016). Identifying anatomical origins of coexisting oscillations in the cortical microcircuit. PLOS Computational Biology 12:e1005132. DOI: 10.1371/journal.pcbi.1005132.
  • Grytskyy D., Diesmann M., Helias M. (2016). Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality. Physical Review E 93. DOI: 10.1103/PhysRevE.93.062303.
  • Hagen E., Dahmen D., Stavrinou ML., Lindén H., Tetzlaff T., van Albada SJ., Grün S., Diesmann M., Einevoll GT. (2016). Hybrid scheme for modeling local field potentials from point-neuron networks. Cerebral Cortex 26:4461–4496. DOI: 10.1093/cercor/bhw237.
  • Pfeil T., Jordan J., Tetzlaff T., Grübl A., Schemmel J., Diesmann M., Meier K. (2016). Effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study. Physical Review X 6. DOI: 10.1103/PhysRevX.6.021023.
  • Trengove C., Diesmann M., Leeuwen C van. (2016). Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains. Journal of Computational Neuroscience 40:1–26. DOI: 10.1007/s10827-015-0581-5.
  • Hahne J., Helias M., Kunkel S., Igarashi J., Bolten M., Frommer A., Diesmann M. (2015). A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics 9:22. DOI: 10.3389/fninf.2015.00022.
  • Muller E., Bednar JA., Diesmann M., Gewaltig M-O., Hines M., Davison AP. (2015). Python in neuroscience. Frontiers in Neuroinformatics 9:11. DOI: 10.3389/fninf.2015.00011.
  • Schuecker J., Diesmann M., Helias M. (2015). Modulated escape from a metastable state driven by colored noise. Physical Review E 92. DOI: 10.1103/PhysRevE.92.052119.
  • van Albada SJ., Helias M., Diesmann M. (2015). Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. PLOS Computational Biology 11:e1004490. DOI: 10.1371/journal.pcbi.1004490.
  • Djurfeldt M., Davison AP., Eppler JM. (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Frontiers in Neuroinformatics 8:43. DOI: 10.3389/fninf.2014.00043.
  • Helias M., Tetzlaff T., Diesmann M. (2014). The correlation structure of local neuronal networks intrinsically results from recurrent dynamics. PLoS Computational Biology 10:e1003428. DOI: 10.1371/journal.pcbi.1003428.
  • Kriener B., Helias M., Rotter S., Diesmann M., Einevoll GT. (2014). How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Frontiers in Computational Neuroscience 7:187. DOI: 10.3389/fncom.2013.00187.
  • Kunkel S., Schmidt M., Eppler JM., Plesser HE., Masumoto G., Igarashi J., Ishii S., Fukai T., Morrison A., Diesmann M., Helias M. (2014). Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics 8:78. DOI: 10.3389/fninf.2014.00078.
  • Potjans TC., Diesmann M. (2014). The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex 24:785–806. DOI: 10.1093/cercor/bhs358.
  • Abeles M., Diesmann M., Flash T., Geisel T., Herrmann M., Teicher M. (2013). Compositionality in neural control: an interdisciplinary study of scribbling movements in primates. Frontiers in Computational Neuroscience 7:103. DOI: 10.3389/fncom.2013.00103.
  • Grytskyy D., Tetzlaff T., Diesmann M., Helias M. (2013). A unified view on weakly correlated recurrent networks. Frontiers in Computational Neuroscience 7:131. DOI: 10.3389/fncom.2013.00131.
  • Helias M., Tetzlaff T., Diesmann M. (2013). Echoes in correlated neural systems. New Journal of Physics 15:023002. DOI: 10.1088/1367-2630/15/2/023002.
  • Schultze-Kraft M., Diesmann M., Grün S., Helias M. (2013). Noise suppression and surplus synchrony by coincidence detection. PLoS Computational Biology 9:e1002904. DOI: 10.1371/journal.pcbi.1002904.
  • Trengove C., van Leeuwen C., Diesmann M. (2013). High-capacity embedding of synfire chains in a cortical network model. Journal of Computational Neuroscience 34:185–209. DOI: 10.1007/s10827-012-0413-9.
  • Vlachos A., Helias M., Becker D., Diesmann M., Deller T. (2013). NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro. Neurobiology of Disease 59:267–276. DOI: 10.1016/j.nbd.2013.07.018.
  • Bakker R., Wachtler T., Diesmann M. (2012). CoCoMac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics 6:30. DOI: 10.3389/fninf.2012.00030.
  • Crook SM., Bednar JA., Berger S., Cannon R., Davison AP., Djurfeldt M., Eppler J., Kriener B., Furber S., Graham B., Plesser HE., Schwabe L., Smith L., Steuber V., Albada S van. (2012). Creating, documenting and sharing network models. Network: Computation in Neural Systems 23:131–149. DOI: 10.3109/0954898X.2012.722743.
  • Deger M., Helias M., Rotter S., Diesmann M. (2012). Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex. PLoS Computational Biology 8:e1002689. DOI: 10.1371/journal.pcbi.1002689.
  • Gerstein GL., Williams ER., Diesmann M., Grün S., Trengove C. (2012). Detecting synfire chains in parallel spike data. Journal of Neuroscience Methods 206:54–64. DOI: 10.1016/j.jneumeth.2012.02.003.
  • Helias M., Kunkel S., Masumoto G., Igarashi J., Eppler JM., Ishii S., Fukai T., Morrison A., Diesmann M. (2012). Supercomputers ready for use as discovery machines for neuroscience. Frontiers in Neuroinformatics 6:26. DOI: 10.3389/fninf.2012.00026.
  • Kunkel S., Potjans TC., Eppler JM., Plesser HE., Morrison A., Diesmann M. (2012). Meeting the memory challenges of brain-scale network simulation. Frontiers in Neuroinformatics 5:35. DOI: 10.3389/fninf.2011.00035.
  • Pfeil T., Potjans TC., Schrader S., Potjans W., Schemmel J., Diesmann M., Meier K. (2012). Is a 4-bit synaptic weight resolution enough? – constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Frontiers in Neuroscience 6:90. DOI: 10.3389/fnins.2012.00090.
  • Tetzlaff T., Helias M., Einevoll GT., Diesmann M. (2012). Decorrelation of neural-network activity by inhibitory feedback. PLoS Computational Biology 8:e1002596. DOI: 10.1371/journal.pcbi.1002596.
  • Anette von K., Tobias R., C PT., Markus D., Torsten K. (2011). Towards the visualization of spiking neurons in virtual reality. Studies in Health Technology and Informatics:685–687. DOI: 10.3233/978-1-60750-706-2-685.
  • Brüderle D., Petrovici MA., Vogginger B., Ehrlich M., Pfeil T., Millner S., Grübl A., Wendt K., Müller E., Schwartz M-O., de Oliveira DH., Jeltsch S., Fieres J., Schilling M., Müller P., Breitwieser O., Petkov V., Muller L., Davison AP., Krishnamurthy P., Kremkow J., Lundqvist M., Muller E., Partzsch J., Scholze S., Zühl L., Mayr C., Destexhe A., Diesmann M., Potjans TC., Lansner A., Schüffny R., Schemmel J., Meier K. (2011). A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biological Cybernetics 104:263–296. DOI: 10.1007/s00422-011-0435-9.
  • Denker M., Roux S., Lindén H., Diesmann M., Riehle A., Grün S. (2011). The local field potential reflects surplus spike synchrony. Cerebral Cortex 21:2681–2695. DOI: 10.1093/cercor/bhr040.
  • Hanuschkin A., Diesmann M., Morrison A. (2011). A reafferent and feed-forward model of song syntax generation in the bengalese finch. Journal of Computational Neuroscience 31:509–532. DOI: 10.1007/s10827-011-0318-z.
  • Hanuschkin A., Herrmann JM., Morrison A., Diesmann M. (2011). Compositionality of arm movements can be realized by propagating synchrony. Journal of Computational Neuroscience 30:675–697. DOI: 10.1007/s10827-010-0285-9.
  • Helias M., Deger M., Rotter S., Diesmann M. (2011). Finite post synaptic potentials cause a fast neuronal response. Frontiers in Neuroscience 5:19. DOI: 10.3389/fnins.2011.00019.
  • Ishii S., Diesmann M., Doya K. (2011). Multi-scale, multi-modal neural modeling and simulation. Neural Networks 24:917. DOI: 10.1016/j.neunet.2011.07.004.
  • Lindén H., Tetzlaff T., Potjans TC., Pettersen KH., Grün S., Diesmann M., Einevoll GT. (2011). Modeling the spatial reach of the lfp. Neuron 72:859–872. DOI: 10.1016/j.neuron.2011.11.006.
  • Potjans W., Diesmann M., Morrison A. (2011). An imperfect dopaminergic error signal can drive temporal-difference learning. PLoS Computational Biology 7:e1001133. DOI: 10.1371/journal.pcbi.1001133.
  • Schrader S., Diesmann M., Morrison A. (2011). A compositionality machine realized by a hierarchic architecture of synfire chains. Frontiers in Computational Neuroscience 4:154. DOI: 10.3389/fncom.2010.00154.
  • Wagatsuma N., Potjans TC., Diesmann M., Fukai T. (2011). Layer-dependent attentional processing by top-down signals in a visual cortical microcircuit model. Frontiers in Computational Neuroscience 5:31. DOI: 10.3389/fncom.2011.00031.
  • Denker M., Riehle A., Diesmann M., Grün S. (2010). Estimating the contribution of assembly activity to cortical dynamics from spike and population measures. Journal of Computational Neuroscience 29:599–613. DOI: 10.1007/s10827-010-0241-8.
  • Djurfeldt M., Hjorth J., Eppler JM., Dudani N., Helias M., Potjans TC., Bhalla US., Diesmann M., Hellgren Kotaleski J., Ekeberg Ö. (2010). Run-time interoperability between neuronal network simulators based on the music framework. Neuroinformatics 8:43–60. DOI: 10.1007/s12021-010-9064-z.
  • Hanuschkin A., Kunkel S., Helias M., Morrison A., Diesmann M. (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers in Neuroinformatics 4:113. DOI: 10.3389/fninf.2010.00113.
  • Helias M., Deger M., Diesmann M., Rotter S. (2010). Equilibrium and response properties of the integrate-and-fire neuron in discrete time. Frontiers in Computational Neuroscience 3:29. DOI: 10.3389/neuro.10.029.2009.
  • Helias M., Deger M., Rotter S., Diesmann M. (2010). Instantaneous non-linear processing by pulse-coupled threshold units. PLoS Computational Biology 6:e1000929. DOI: 10.1371/journal.pcbi.1000929.
  • Kilavik BE., Confais J., Ponce-Alvarez A., Diesmann M., Riehle A. (2010). Evoked potentials in motor cortical local field potentials reflect task timing and behavioral performance. Journal of Neurophysiology 104:2338–2351. DOI: 10.1152/jn.00250.2010.
  • Kunkel S., Diesmann M., Morrison A. (2010). Limits to the development of feed-forward structures in large recurrent neuronal networks. Frontiers in Computational Neuroscience 4:160. DOI: 10.3389/fncom.2010.00160.
  • Louis SG., Gerstein GL., Grün S., Diesmann M. (2010). Surrogate spike train generation through dithering in operational time. Frontiers in Computational Neuroscience 4:127. DOI: 10.3389/fncom.2010.00127.
  • Nordlie E., Plesser HE. (2010). Visualizing neuronal network connectivity with connectivity pattern tables. Frontiers in Neuroinformatics 3:39. DOI: 10.3389/neuro.11.039.2009.
  • Potjans W., Morrison A., Diesmann M. (2010). Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Frontiers in Computational Neuroscience 4:141. DOI: 10.3389/fncom.2010.00141.
2009 2008
  • Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower JM., Diesmann M., Morrison A., Goodman PH., Harris FC., Zirpe M., Natschläger T., Pecevski D., Ermentrout B., Djurfeldt M., Lansner A., Rochel O., Vieville T., Muller E., Davison AP., El Boustani S., Destexhe A. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience 23:349–398. DOI: 10.1007/s10827-007-0038-6.
  • Eppler JM., Helias M., Muller E., Diesmann M., Gewaltig M-O. (2008). PyNEST: a convenient interface to the nest simulator. Frontiers in Neuroinformatics 2:12. DOI: 10.3389/neuro.11.012.2008.
  • Goedeke S., Diesmann M. (2008). The mechanism of synchronization in feed-forward neuronal networks. New Journal of Physics 10:015007. DOI: 10.1088/1367-2630/10/1/015007.
  • Helias M., Rotter S., Gewaltig M-O., Diesmann M. (2008). Structural plasticity controlled by calcium based correlation detection. Frontiers in Computational Neuroscience 2:7. DOI: 10.3389/neuro.10.007.2008.
  • Kriener B., Tetzlaff T., Aertsen A., Diesmann M., Rotter S. (2008). Correlations and population dynamics in cortical networks. Neural Computation 20:2185–2226. DOI: 10.1162/neco.2008.02-07-474.
  • Morrison A., Diesmann M., Gerstner W. (2008). Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 98:459–478. DOI: 10.1007/s00422-008-0233-1.
  • Pazienti A., Maldonado PE., Diesmann M., Grün S. (2008). Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research 1225:39–46. DOI: 10.1016/j.brainres.2008.04.073.
  • Schrader S., Grun S., Diesmann M., Gerstein GL. (2008). Detecting synfire chain activity using massively parallel spike train recording. Journal of Neurophysiology 100:2165–2176. DOI: 10.1152/jn.01245.2007.
  • Tetzlaff T., Rotter S., Stark E., Abeles M., Aertsen A., Diesmann M. (2008). Dependence of neuronal correlations on filter characteristics and marginal spike train statistics. Neural Computation 20:2133–2184. DOI: 10.1162/neco.2008.05-07-525.
  • Morrison A., Aertsen A., Diesmann M. (2007). Spike-timing-dependent plasticity in balanced random networks. Neural Computation 19:1437–1467. DOI: 10.1162/neco.2007.19.6.1437.
  • Morrison A., Straube S., Plesser HE., Diesmann M. (2007). Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Computation 19:47–79. DOI: 10.1162/neco.2007.19.1.47.
  • Backofen R., Borrmann H-G., Deck W., Dedner A., Raedt LD., Desch K., Diesmann M., Geier M., Greiner A., Hess WR., Honerkamp J., Jankowski S., Krossing I., Liehr AW., Karwath A., Klöfkorn R., Pesché R., Potjans T., Röttger MC., Schmidt-Thieme L., Schneider G., Voß B., Wiebelt B., Wienemann P., Winterer V-H. (2006). A bottom-up approach to grid-computing at a university: the black-forest-grid initiative. PIK - Praxis der Informationsverarbeitung und Kommunikation 29:81–87. DOI: 10.1515/PIKO.2006.81.
  • Guerrero-Rivera R., Morrison A., Diesmann M., Pearce TC. (2006). Programmable logic construction kits for hyper-real-time neuronal modeling. Neural Computation 18:2651–2679. DOI: 10.1162/neco.2006.18.11.2651.
  • Morrison A., Mehring C., Geisel T., Aertsen A., Diesmann M. (2005). Advancing the boundaries of high-connectivity network simulation with distributed computing. Neural Computation 17:1776–1801. DOI: 10.1162/0899766054026648.
  • Denker M., Timme M., Diesmann M., Wolf F., Geisel T. (2004). Breaking synchrony by heterogeneity in complex networks. Physical Review Letters 92. DOI: 10.1103/PhysRevLett.92.074103.
  • Tetzlaff T., Morrison A., Geisel T., Diesmann M. (2004). Consequences of realistic network size on the stability of embedded synfire chains. Neurocomputing 58–60:117–121. DOI: 10.1016/j.neucom.2004.01.031.
  • Grün S., Riehle A., Aertsen A., Diesmann M. (2003). Temporal scales of cortical interactions. Nova Acta Leopoldina NF 88:189–206.
  • Grün S., Riehle A., Diesmann M. (2003). Effect of cross-trial nonstationarity on joint-spike events. Biological Cybernetics 88:335–351. DOI: 10.1007/s00422-002-0386-2.
  • Mehring C., Hehl U., Kubo M., Diesmann M., Aertsen A. (2003). Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biological Cybernetics 88:395–408. DOI: 10.1007/s00422-002-0384-4.
  • Pipa G., Diesmann M., Grün S. (2003). Significance of joint-spike events based on trial-shuffling by efficient combinatorial methods. Complexity 8:79–86. DOI: 10.1002/cplx.10085.
  • Tetzlaff T., Buschermöhle M., Geisel T., Diesmann M. (2003). The spread of rate and correlation in stationary cortical networks. Neurocomputing 52–54:949–954. DOI: 10.1016/S0925-2312(02)00854-8.
  • Egert U., Knott T., Schwarz C., Nawrot M., Brandt A., Rotter S., Diesmann M. (2002). MEA-tools: an open source toolbox for the analysis of multi-electrode data with matlab. Journal of Neuroscience Methods 117:33–42. DOI: 10.1016/S0165-0270(02)00045-6.
  • Grün S., Diesmann M., Aertsen A. (2002a). Unitary events in multiple single-neuron spiking activity: I. detection and significance. Neural Computation 14:43–80. DOI: 10.1162/089976602753284455.
  • Grün S., Diesmann M., Aertsen A. (2002b). Unitary events in multiple single-neuron spiking activity: II. nonstationary data. Neural Computation 14:81–119. DOI: 10.1162/089976602753284464.
  • Tetzlaff T., Geisel T., Diesmann M. (2002). The ground state of cortical feed-forward networks. Neurocomputing 44–46:673–678. DOI: 10.1016/S0925-2312(02)00456-3.
  • Diesmann M., Gewaltig M-O., Rotter S., Aertsen A. (2001). State space analysis of synchronous spiking in cortical neural networks. Neurocomputing 38–40:565–571. DOI: 10.1016/S0925-2312(01)00409-X.
  • Gewaltig M-O., Diesmann M., Aertsen A. (2001a). Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. Neural Networks 14:657–673. DOI: 10.1016/S0893-6080(01)00070-3.
  • Gewaltig M-O., Diesmann M., Aertsen A. (2001b). Cortical synfire-activity: configuration space and survival probability. Neurocomputing 38–40:621–626. DOI: 10.1016/S0925-2312(01)00454-4.
  • Riehle A., Grammont F., Diesmann M., Grün S. (2000). Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation. Journal of Physiology-Paris 94:569–582. DOI: 10.1016/S0928-4257(00)01100-1.
  • Diesmann M., Gewaltig M-O., Aertsen A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature 402:529–533. DOI: 10.1038/990101.
  • Grün S., Diesmann M., Grammont F., Riehle A., Aertsen A. (1999). Detecting unitary events without discretization of time. Journal of Neuroscience Methods 94:67–79. DOI: 10.1016/S0165-0270(99)00126-0.
  • Rotter S., Diesmann M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biological Cybernetics 81:381–402. DOI: 10.1007/s004220050570.
  • Riehle A., Grün S., Diesmann M., Aertsen A. (1997). Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278:1950–1953. DOI: 10.1126/science.278.5345.1950.
  • Aertsen A., Diesmann M., Gewaltig M. (1996). Propagation of synchronous spiking activity in feedforward neural networks. Journal of Physiology-Paris 90:243–247. DOI: 10.1016/S0928-4257(97)81432-5.