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Items: 1 to 50 of 89

1.

Second type of criticality in the brain uncovers rich multiple-neuron dynamics.

Dahmen D, Grün S, Diesmann M, Helias M.

Proc Natl Acad Sci U S A. 2019 Jun 25;116(26):13051-13060. doi: 10.1073/pnas.1818972116. Epub 2019 Jun 12.

2.

The Scientific Case for Brain Simulations.

Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F.

Neuron. 2019 May 22;102(4):735-744. doi: 10.1016/j.neuron.2019.03.027. Review.

PMID:
31121126
3.

Computational Neuroscience: Mathematical and Statistical Perspectives.

Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, Teramae JN, Thomas PJ, Reimers M, Rodu J, Rotstein HG, Shea-Brown E, Shimazaki H, Shinomoto S, Yu BM, Kramer MA.

Annu Rev Stat Appl. 2018 Mar;5:183-214. doi: 10.1146/annurev-statistics-041715-033733. Epub 2017 Dec 8.

4.

VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output.

Senk J, Carde C, Hagen E, Kuhlen TW, Diesmann M, Weyers B.

Front Neuroinform. 2018 Nov 8;12:75. doi: 10.3389/fninf.2018.00075. eCollection 2018.

5.

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.

Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ.

PLoS Comput Biol. 2018 Oct 18;14(10):e1006359. doi: 10.1371/journal.pcbi.1006359. eCollection 2018 Oct.

6.

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models.

Maksimov A, Diesmann M, van Albada SJ.

Front Comput Neurosci. 2018 Jul 10;12:44. doi: 10.3389/fncom.2018.00044. eCollection 2018.

7.

Corrigendum: Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S.

Front Neuroinform. 2018 Jul 4;12:34. doi: 10.3389/fninf.2018.00034. eCollection 2018.

8.

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model.

van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB, Lester DR, Diesmann M, Furber SB.

Front Neurosci. 2018 May 23;12:291. doi: 10.3389/fnins.2018.00291. eCollection 2018.

9.

LFP beta amplitude is linked to mesoscopic spatio-temporal phase patterns.

Denker M, Zehl L, Kilavik BE, Diesmann M, Brochier T, Riehle A, Grün S.

Sci Rep. 2018 Mar 26;8(1):5200. doi: 10.1038/s41598-018-22990-7.

10.

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S.

Front Neuroinform. 2018 Feb 16;12:2. doi: 10.3389/fninf.2018.00002. eCollection 2018. Erratum in: Front Neuroinform. 2018 Jul 04;12:34.

11.

Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons.

Krishnan J, Porta Mana P, Helias M, Diesmann M, Di Napoli E.

Front Neuroinform. 2018 Jan 5;11:75. doi: 10.3389/fninf.2017.00075. eCollection 2017.

12.

Multi-scale account of the network structure of macaque visual cortex.

Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ.

Brain Struct Funct. 2018 Apr;223(3):1409-1435. doi: 10.1007/s00429-017-1554-4. Epub 2017 Nov 16.

13.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M, Diesmann M.

Front Neuroinform. 2017 May 24;11:34. doi: 10.3389/fninf.2017.00034. eCollection 2017.

14.

Constructing Neuronal Network Models in Massively Parallel Environments.

Ippen T, Eppler JM, Plesser HE, Diesmann M.

Front Neuroinform. 2017 May 16;11:30. doi: 10.3389/fninf.2017.00030. eCollection 2017.

15.

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.

Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M.

PLoS Comput Biol. 2017 Feb 1;13(2):e1005179. doi: 10.1371/journal.pcbi.1005179. eCollection 2017 Feb.

16.

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination.

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.

Neuron. 2016 Nov 2;92(3):628-631. doi: 10.1016/j.neuron.2016.10.035.

17.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

Hagen E, Dahmen D, Stavrinou ML, Lindén H, Tetzlaff T, van Albada SJ, Grün S, Diesmann M, Einevoll GT.

Cereb Cortex. 2016 Dec;26(12):4461-4496. doi: 10.1093/cercor/bhw237. Epub 2016 Oct 20.

18.

Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit.

Bos H, Diesmann M, Helias M.

PLoS Comput Biol. 2016 Oct 13;12(10):e1005132. doi: 10.1371/journal.pcbi.1005132. eCollection 2016 Oct.

19.

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality.

Grytskyy D, Diesmann M, Helias M.

Phys Rev E. 2016 Jun;93(6):062303. doi: 10.1103/PhysRevE.93.062303. Epub 2016 Jun 6.

PMID:
27415276
20.

Modulated escape from a metastable state driven by colored noise.

Schuecker J, Diesmann M, Helias M.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015;92(5):052119. doi: 10.1103/PhysRevE.92.052119. Epub 2015 Nov 16.

PMID:
26651659
21.

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.

Trengove C, Diesmann M, van Leeuwen C.

J Comput Neurosci. 2016 Feb;40(1):1-26. doi: 10.1007/s10827-015-0581-5. Epub 2015 Nov 11.

22.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M.

Front Neuroinform. 2015 Sep 9;9:22. doi: 10.3389/fninf.2015.00022. eCollection 2015.

23.

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations.

van Albada SJ, Helias M, Diesmann M.

PLoS Comput Biol. 2015 Sep 1;11(9):e1004490. doi: 10.1371/journal.pcbi.1004490. eCollection 2015 Sep.

24.

Python in neuroscience.

Muller E, Bednar JA, Diesmann M, Gewaltig MO, Hines M, Davison AP.

Front Neuroinform. 2015 Apr 14;9:11. doi: 10.3389/fninf.2015.00011. eCollection 2015. No abstract available.

25.

Spiking network simulation code for petascale computers.

Kunkel S, Schmidt M, Eppler JM, Plesser HE, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M, Helias M.

Front Neuroinform. 2014 Oct 10;8:78. doi: 10.3389/fninf.2014.00078. eCollection 2014.

26.

How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime.

Kriener B, Helias M, Rotter S, Diesmann M, Einevoll GT.

Front Comput Neurosci. 2014 Jan 7;7:187. doi: 10.3389/fncom.2013.00187. eCollection 2013.

27.

The correlation structure of local neuronal networks intrinsically results from recurrent dynamics.

Helias M, Tetzlaff T, Diesmann M.

PLoS Comput Biol. 2014 Jan;10(1):e1003428. doi: 10.1371/journal.pcbi.1003428. Epub 2014 Jan 16.

28.

Spatial and feature-based attention in a layered cortical microcircuit model.

Wagatsuma N, Potjans TC, Diesmann M, Sakai K, Fukai T.

PLoS One. 2013 Dec 6;8(12):e80788. doi: 10.1371/journal.pone.0080788. eCollection 2013.

29.

A unified view on weakly correlated recurrent networks.

Grytskyy D, Tetzlaff T, Diesmann M, Helias M.

Front Comput Neurosci. 2013 Oct 18;7:131. doi: 10.3389/fncom.2013.00131. eCollection 2013.

30.

Compositionality in neural control: an interdisciplinary study of scribbling movements in primates.

Abeles M, Diesmann M, Flash T, Geisel T, Herrmann M, Teicher M.

Front Comput Neurosci. 2013 Sep 12;7:103. doi: 10.3389/fncom.2013.00103. eCollection 2013.

31.

NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro.

Vlachos A, Helias M, Becker D, Diesmann M, Deller T.

Neurobiol Dis. 2013 Nov;59:267-76. doi: 10.1016/j.nbd.2013.07.018. Epub 2013 Aug 9.

PMID:
23932917
32.

Noise suppression and surplus synchrony by coincidence detection.

Schultze-Kraft M, Diesmann M, Grün S, Helias M.

PLoS Comput Biol. 2013 Apr;9(4):e1002904. doi: 10.1371/journal.pcbi.1002904. Epub 2013 Apr 4.

33.

CoCoMac 2.0 and the future of tract-tracing databases.

Bakker R, Wachtler T, Diesmann M.

Front Neuroinform. 2012 Dec 27;6:30. doi: 10.3389/fninf.2012.00030. eCollection 2012.

34.

The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model.

Potjans TC, Diesmann M.

Cereb Cortex. 2014 Mar;24(3):785-806. doi: 10.1093/cercor/bhs358. Epub 2012 Dec 2.

35.

Decorrelation of neural-network activity by inhibitory feedback.

Tetzlaff T, Helias M, Einevoll GT, Diesmann M.

PLoS Comput Biol. 2012 Aug;8(8):e1002596. doi: 10.1371/journal.pcbi.1002596. Epub 2012 Aug 2.

36.

Supercomputers ready for use as discovery machines for neuroscience.

Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A, Diesmann M.

Front Neuroinform. 2012 Nov 2;6:26. doi: 10.3389/fninf.2012.00026. eCollection 2012.

37.

Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex.

Deger M, Helias M, Rotter S, Diesmann M.

PLoS Comput Biol. 2012;8(9):e1002689. doi: 10.1371/journal.pcbi.1002689. Epub 2012 Sep 20.

38.

High-capacity embedding of synfire chains in a cortical network model.

Trengove C, van Leeuwen C, Diesmann M.

J Comput Neurosci. 2013 Apr;34(2):185-209. doi: 10.1007/s10827-012-0413-9. Epub 2012 Aug 11.

39.

Is a 4-bit synaptic weight resolution enough? - constraints on enabling spike-timing dependent plasticity in neuromorphic hardware.

Pfeil T, Potjans TC, Schrader S, Potjans W, Schemmel J, Diesmann M, Meier K.

Front Neurosci. 2012 Jul 17;6:90. doi: 10.3389/fnins.2012.00090. eCollection 2012.

40.

Detecting synfire chains in parallel spike data.

Gerstein GL, Williams ER, Diesmann M, Grün S, Trengove C.

J Neurosci Methods. 2012 Apr 30;206(1):54-64. doi: 10.1016/j.jneumeth.2012.02.003. Epub 2012 Feb 15.

41.

Meeting the memory challenges of brain-scale network simulation.

Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A, Diesmann M.

Front Neuroinform. 2012 Jan 24;5:35. doi: 10.3389/fninf.2011.00035. eCollection 2011.

42.

Modeling the spatial reach of the LFP.

Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M, Einevoll GT.

Neuron. 2011 Dec 8;72(5):859-72. doi: 10.1016/j.neuron.2011.11.006.

43.

Multi-scale, multi-modal neural modeling and simulation.

Ishii S, Diesmann M, Doya K.

Neural Netw. 2011 Nov;24(9):917. doi: 10.1016/j.neunet.2011.07.004. Epub 2011 Jul 14. No abstract available.

PMID:
21840687
44.

Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model.

Wagatsuma N, Potjans TC, Diesmann M, Fukai T.

Front Comput Neurosci. 2011 Jul 8;5:31. doi: 10.3389/fncom.2011.00031. eCollection 2011.

45.

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.

Brüderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, 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.

Biol Cybern. 2011 May;104(4-5):263-96. doi: 10.1007/s00422-011-0435-9. Epub 2011 May 27.

PMID:
21618053
46.

An imperfect dopaminergic error signal can drive temporal-difference learning.

Potjans W, Diesmann M, Morrison A.

PLoS Comput Biol. 2011 May;7(5):e1001133. doi: 10.1371/journal.pcbi.1001133. Epub 2011 May 12.

47.

The local field potential reflects surplus spike synchrony.

Denker M, Roux S, Lindén H, Diesmann M, Riehle A, Grün S.

Cereb Cortex. 2011 Dec;21(12):2681-95. doi: 10.1093/cercor/bhr040. Epub 2011 Apr 20.

48.

Finite post synaptic potentials cause a fast neuronal response.

Helias M, Deger M, Rotter S, Diesmann M.

Front Neurosci. 2011 Feb 24;5:19. doi: 10.3389/fnins.2011.00019. eCollection 2011.

49.

Limits to the development of feed-forward structures in large recurrent neuronal networks.

Kunkel S, Diesmann M, Morrison A.

Front Comput Neurosci. 2011 Feb 14;4:160. doi: 10.3389/fncom.2010.00160. eCollection 2011.

50.

A reafferent and feed-forward model of song syntax generation in the Bengalese finch.

Hanuschkin A, Diesmann M, Morrison A.

J Comput Neurosci. 2011 Nov;31(3):509-32. doi: 10.1007/s10827-011-0318-z. Epub 2011 Mar 15.

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