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Peer-reviewed articles of Theory of Multi-Scale Neuronal Networks

  • Dahmen D., Layer M., Deutz L., Dabrowska P.A., Voges N., von Papen M., Brochier T., Riehle A., Diesmann M., Grün S., Helias M. (2022) Global organization of neuronal activity only requires unstructured local connectivity. eLife 2022;11:e68422. DOI:10.7554/eLife.68422
  • Keup C., Kühn T., Dahmen D. and Helias M. (2021) Transient Chaotic Dimensionality Expansion by Recurrent Networks. Phys. Rev. X 11, 021064. DOI:10.1103/PhysRevX.11.021064
  • Stapmanns J., Hahne J., Helias M., Bolten M., Diesmann M. and Dahmen D. (2021) Event-Based Update of Synapses in Voltage-Based Learning Rules. Front. Neuroinform. 15:609147. DOI:10.3389/fninf.2021.609147
  • van Meegen A., Kühn T., Helias, M. (2021) Large-Deviation Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions. Physical Review Letters 127(15), 158302. DOI:10.1103/PhysRevLett.127.158302
  • Dahmen D., Gilson M., Helias M. (2020) Capacity of the covariance perceptron Journal of physics A 53(35), 354002 DOI:10.1088/1751-8121/ab82dd
  • Gilson M., Dahmen D., Moreno-Bote R., Insabato A., Helias M. (2020) The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks. PLoS Comput Biol. DOI:10.1371/journal.pcbi.1008127
  • Helias M. (2020) Momentum-dependence in the infinitesimal Wilsonian renormalization group. Journal of Physics A 53(44) DOI:10.1088/1751-8121/abb169
  • Jordan J., Helias M., Diesmann M. and Kunkel S. (2020) Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Front. Neuroinform. 14:12. DOI:10.3389/fninf.2020.00012
  • Nestler S., Keup C., Dahmen D., Gilson M., Rauhut H., Helias M. (2020) Unfolding recurrence by Green’s functions for optimized reservoir computing. 34th Conference on Neural Information Processing Systems, NeurIPS 2020, online, 6 Dec 2020 - 12 Dec 2020, 1pp.
  • René A., Longtin A., Macke J.H. (2020) Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Comput. 32(8):1448-1498 DOI:10.1162/neco_a_01292
  • Senk J., Korvasova K., Schuecker J., Hagen E., Tetzlaff T., Diesmann M., Helias M.(2020) Conditions for wave trains in spiking neural networks. Phys. Rev. Research 2:023174 DOI:10.1103/PhysRevResearch.2.023174
  • Stapmanns J., Kühn T., Dahmen D., Luu T., Honercamp C., Helias, M. (2020) Self-consistent formulations for stochastic nonlinear neuronal dynamics Phys. Rev. E 101(4) DOI:10.1103/PhysRevE.101.042124
  • Dahmen D., Grün S., Diesmann M. , Helias M. (2019) Second type of criticality in the brain uncovers rich multiple-neuron dynamics Proceedings of the National Academy of Sciences of the United States of America 116 (26):13051-13060DOI:10.1073/pnas.1818972116
  • Heers, M., M. Helias, T. Hedrich, M. Dümpelmann, A. Schulze-Bonhage, and T. Ball (2018) Spectral bandwidth of interictal fast epileptic activity characterizes the seizure onset zone. NeuroImage: Clinical, 2018. 17: p. 865-872. DOI:10.1016/j.nicl.2017.11.021
  • Jordan J., Ippen T., Helias M., Kitayama I., Sato M., Igarashi J., Diesmann M., Kunkel S. (2018). Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics 12:2. DOI:10.3389/fninf.2018.00002
  • 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. (2018). Computational neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application 5:183-214. DOI: 10.1146/annurev-statistics-041715-033733.
  • Krishnan J., Porta Mana PGL., Helias M., Diesmann M., Di Napoli E. (2018). Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. Frontiers in Neuroinformatics 11:75. doi:10.3389/fninf.2017.00075.
  • Kühn T., Helias M. (2018). Expansion of the effective action around non-Gaussian theories. Journal of Physics A: Mathematical and Theoretical 51:375004. doi:10.1088/1751-8121/aad52e.
  • Schuecker J., Goedeke S., Helias M. (2018). Optimal Sequence Memory in Driven Random Networks. Phys. Rev. X 8, 041029. DOI: 10.1103/PhysRevX.8.041029
  • Völker M., Fiederer LDJ., Berberich S., Hammer J., Behncke J., Krsek P., Tomasek M., Marusic P., Reinacher PC., Coenen VA., Helias M., Schulze-Bornhage A., Burgard W., Ball T. (2018) The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG. NeuroImage 173:564-579. doi:10.1016/j.neuroimage.2018.01.059
  • 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.
  • Heers M., Helias M., Hedrich T., Dümpelmann M., Schulze-Bonhage A., Ball T. (2017) Spectral bandwidth of interictal fast epileptic activity characterizes the seizure onset zone. NeuroImage: Clinical 17:865-872. doi:10.1016/j.nicl.2017.11.021
  • Kühn T., Helias M. (2017). Locking of correlated neural activity to ongoing oscillations. PLOS Computational Biology 13:e1005534. DOI: 10.1371/journal.pcbi.1005534.
  • Rostami V., Porta Mana P., Grün S., Helias M. (2017b). Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. PLOS Computational Biology 13(10): e1005762. .DOI: 10.1371/journal.pcbi.1005762.
  • 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.
  • 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.
  • Dahmen D., Bos H., Helias M. (2016). Correlated fluctuations in strongly coupled binary networks beyond equilibrium. Physical Review X 6. DOI: 10.1103/PhysRevX.6.031024.
  • 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.
  • Torre E., Canova C., Denker M., Gerstein G., Helias M., Grün S. (2016). ASSET: analysis of sequences of synchronous events in massively parallel spike trains. PLOS Computational Biology 12:e1004939. DOI: 10.1371/journal.pcbi.1004939.
  • Chua Y., Morrison A., Helias M. (2015). Modeling the calcium spike as a threshold triggered fixed waveform for synchronous inputs in the fluctuation regime. Frontiers in Computational Neuroscience 9:91. DOI: 10.3389/fncom.2015.00091.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Deger M., Helias M., Boucsein C., Rotter S. (2011). Statistical properties of superimposed stationary spike trains. Journal of Computational Neuroscience 32:443–463. DOI: 10.1007/s10827-011-0362-8.
  • 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.
  • Deger M., Helias M., Cardanobile S., Atay FM., Rotter S. (2010). Nonequilibrium dynamics of stochastic point processes with refractoriness. Physical Review E 82. DOI: 10.1103/PhysRevE.82.021129.
  • 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., Rotter S., Diesmann M. (2010a). Instantaneous non-linear processing by pulse-coupled threshold units. PLoS Computational Biology 6:e1000929. DOI: 10.1371/journal.pcbi.1000929.
  • Helias M., Deger M., Diesmann M., Rotter S. (2009). 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.
  • Kriener B., Helias M., Aertsen A., Rotter S. (2009). Correlations in spiking neuronal networks with distance dependent connections. Journal of Computational Neuroscience 27:177–200. DOI: 10.1007/s10827-008-0135-1.
  • Clemens M., Helias M., Steinmetz T., Wimmer G. (2008). Multiple right-hand side techniques for the numerical simulation of quasistatic electric and magnetic fields. Journal of Computational and Applied Mathematics 215:328–338. DOI: 10.1016/
  • 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.
  • 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.
  • Steinmetz T., Helias M., Wimmer G., Fichte LO., Clemens M. (2006). Electro-quasistatic field simulations based on a discrete electromagnetism formulation. IEEE Transactions on Magnetics 42:755–758. DOI: 10.1109/TMAG.2006.872488.