link to homepage

Institute of Neuroscience and Medicine

Navigation and service

All publications of INM-6 / IAS-6 / INM-10

  • van Albada SJ., Rowley AG., Senk J., Hopkins M., Schmidt M., Stokes AB., Lester DR., Diesmann M., Furber SB. (2018) Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model. Frontiers in Neuroscience 12:291 DOI:10.3389/fnins.2018.00291
  • Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Rübel O., Simon HD., Sommer FT., Prabhat (2018). International Neuroscience Inititatives through the Lens of High-Performance Computing. IEEE Computer
  • 51:50-59 DOI:10.1109/MC.2018.2141039
  • Brochier T., Zehl L., Hao Y., Duret M., Sprenger J., Denker M., Grün S., Riehle A. (2018) Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. (Data publication) Scientific Data 5:180055 DOI:10.1038/sdata.2018.55. Data available at
  • Denker M., Zehl L., Kilavik BE., Diesmann M., Brochier T., Riehle A., Grün S. (2018) LFP beta amplitude is linked to mesoscopic spatio-temporal phase patterns. Scientific Reports 8:5200. DOI:10.1038/s41598-018-22990-7
  • 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. 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.
  • Maksimov A., Diesmann M., van Albada SJ. (2018) Criteria on balance, stability and excitability in cortical networks for constraining computational models Front Comput Neurosci. 12:44 DOI:10.3389/fncom.2018.00044
  • Quaglio P., Rostami V., Torre E., Grün S. (2018) Methods for identification of spike patterns in massively parallel spike trains Biological Cybernetics pp. 1-24. DOI:10.1007/s00422-018-0755-0
  • Riehle A., Brochier TG., Nawrot M., Grün S. (2018) Behavioral context determines network state and variability dynamics in monkey motor cortex. Front. Neural Circuits 12:52 DOI:10.3389/fncir.2018.00052
  • Schmidt M., Bakker R., Hilgetag CC., Diesmann M., van Albada SJ. (2018). Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function 223:1409-1435. DOI:10.1007/s00429-017-1554-4.
  • Senden M., Schuecker J., Hahne J., Diesmann M., Goebel R. (2018) [Re] A neural model of the saccade generator in the reticular formation. ReScience 3:1-12 DOI: 10.5281/zenodo.1241004
  • 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
  • Bahuguna J., Tetzlaff T., Kumar A., Hellgren Kotaleski J., Morrison A. (2017). Homologous basal ganglia network models in physiological and parkinsonian conditions. Frontiers in Computational Neuroscience 11:79. DOI: 10.3389/fncom.2017.00079.
  • Bezgin G., Solodkin A., Bakker R., Ritter P., McIntosh AR. (2017). Mapping complementary features of cross-species structural connectivity to construct realistic “virtual brains”: multimodal structural connectivity for realistic virtual brains. Human Brain Mapping 38:2080–2093. DOI: 10.1002/hbm.23506.
  • Denker M., Zehl L., Bjørg K., Diesmann M., Brochier T., Riehle A., Grün S. (2017). LFP beta amplitude is predictive of mesoscopic spatio-temporal phase patterns. arXiv:1703.09488.
  • Duarte R., Seeholzer A., Zilles K., Morrison A. (2017). Synaptic patterning and the timescales of cortical dynamics. Current opinion in neurobiology 43:156–165. DOI: 10.1016/j.conb.2017.02.007.
  • 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
  • Hinne M., Meijers A., Bakker R., Tiesinga PHE., Mørup M., van Gerven MAJ. (2017). The missing link: predicting connectomes from noisy and partially observed tract tracing data. PLOS Computational Biology 13:e1005374. DOI: 10.1371/journal.pcbi.1005374.
  • 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.
  • Ito J., Yamane Y., Suzuki M., Maldonado P., Fujita I., Tamura H., Grün S. (2017). Switch from ambient to focal processing mode explains the dynamics of free viewing eye movements. Scientific Reports 7. DOI: 10.1038/s41598-017-01076-w.
  • 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.
  • Kühn T., Helias M. (2017). Locking of correlated neural activity to ongoing oscillations. PLOS Computational Biology 13:e1005534. DOI: 10.1371/journal.pcbi.1005534.
  • Müller EJ., van Albada SJ., Kim JW., Robinson PA. (2017). Unified neural field theory of brain dynamics underlying oscillations in parkinson’s disease and generalized epilepsies. Journal of Theoretical Biology 428:132–146. DOI: 10.1016/j.jtbi.2017.06.016.
  • Quaglio P., Yegenoglu A., Torre E., Endres DM., Grün S. (2017). Detection and evaluation of spatio-temporal spike patterns in massively parallel spike train data with spade. Frontiers in Computational Neuroscience 11:41. DOI: 10.3389/fncom.2017.00041.
  • Rostami V., Ito J., Denker M., Grün S. (2017a). [Re] spike synchronization and rate modulation differentially involved in motor cortical function. Rescience 3. DOI: 10.5281/zenodo.583814.
  • 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.
  • Senk J., Yegenoglu A., Amblet O., Brukau Y., Davison A., Lester DR., Lührs A., Quaglio P., Rostami V., Rowley A., Schuller B., Stokes AB., van Albada SJ., Zielasko D., Diesmann M., Weyers B., Denker M., Grün S. (2017). A collaborative simulation-analysis workflow for computational neuroscience using HPC. In: Di Napoli E, Hermanns M-A, Iliev H, Lintermann A, Peyser A eds. High-Performance Scientific Computing. Cham: Springer International Publishing, 243–256. DOI: 10.1007/978-3-319-53862-4_21.
  • Spreizer S., Angelhuber M., Bahuguna J., Aertsen A., Kumar A. (2017). Activity dynamics and signal representation in a striatal network model with distance-dependent connectivity. eneuro:ENEURO.0348-16.2017. DOI: 10.1523/ENEURO.0348-16.2017.
  • von Papen M., Dafsari H., Florin E., Gerick F., Timmermann L., Saur J. (2017). Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components. Journal of Neuroscience Methods 291:198-212. DOI:10.1016/j.jneumeth.2017.08.021
  • 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.
  • 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.
  • Chua Y., Morrison A. (2016). Effects of calcium spikes in the layer 5 pyramidal neuron on coincidence detection and activity propagation. Frontiers in Computational Neuroscience 10:76. DOI: 10.3389/fncom.2016.00076.
  • Diaz-Pier S., Naveau M., Butz-Ostendorf M., Morrison A. (2016). Automatic generation of connectivity for large-scale neuronal network models through structural plasticity. Frontiers in Neuroanatomy 10:57. DOI: 10.3389/fnana.2016.00057.
  • 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.
  • Maksimov A, van Albada SJ, Diesmann M, (2016) [Re] Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model. Rescience. DOI:10.5281/zenodo.161526
  • Mochizuki Y., Onaga T., Shimazaki H., Shimokawa T., Tsubo Y., Kimura R., Saiki A., Sakai Y., Isomura Y., Fujisawa S., Shibata K-I., Hirai D., Furuta T., Kaneko T., Takahashi S., Nakazono T., Ishino S., Sakurai Y., Kitsukawa T., Lee JW., Lee H., Jung MW., Babul C., Maldonado PE., Takahashi K., Arce-McShane FI., Ross CF., Sessle BJ., Hatsopoulos NG., Brochier T., Riehle A., Chorley P., Grün S., Nishijo H., Ichihara-Takeda S., Funahashi S., Shima K., Mushiake H., Yamane Y., Tamura H., Fujita I., Inaba N., Kawano K., Kurkin S., Fukushima K., Kurata K., Taira M., Tsutsui K-I., Ogawa T., Komatsu H., Koida K., Toyama K., Richmond BJ., Shinomoto S. (2016). Similarity in neuronal firing regimes across mammalian species. Journal of Neuroscience 36:5736–5747. DOI: 10.1523/JNEUROSCI.0230-16.2016.
  • Morita K., Jitsev J., Morrison A. (2016). Corticostriatal circuit mechanisms of value-based action selection: implementation of reinforcement learning algorithms and beyond. Behavioural Brain Research 311:110–121. DOI: 10.1016/j.bbr.2016.05.017.
  • 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.
  • Torre E., Canova C., Denker M., Gerstein G., Helias M., Grün S. (2016b). ASSET: analysis of sequences of synchronous events in massively parallel spike trains. PLOS Computational Biology 12:e1004939. DOI: 10.1371/journal.pcbi.1004939.
  • Torre E., Quaglio P., Denker M., Brochier T., Riehle A., Grün S. (2016a). Synchronous spike patterns in macaque motor cortex during an instructed-delay reach-to-grasp task. Journal of Neuroscience 36:8329–8340. DOI: 10.1523/JNEUROSCI.4375-15.2016.
  • 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.
  • Weidel P., Djurfeldt M., Duarte RC., Morrison A. (2016). Closed loop interactions between spiking neural network and robotic simulators based on music and ros. Frontiers in Neuroinformatics 10:31. DOI: 10.3389/fninf.2016.00031.
  • Zehl L., Jaillet F., Stoewer A., Grewe J., Sobolev A., Wachtler T., Brochier TG., Riehle A., Denker M., Grün S. (2016). Handling metadata in a neurophysiology laboratory. Frontiers in Neuroinformatics 10:26. DOI: 10.3389/fninf.2016.00026.
  • Bahuguna J., Aertsen A., Kumar A. (2015). Existence and control of go/no-go decision transition threshold in the striatum. PLOS Computational Biology 11:e1004233. DOI: 10.1371/journal.pcbi.1004233.
  • 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.
  • Duarte R. (2015). Expansion and state-dependent variability along sensory processing streams. Journal of Neuroscience 35:7315–7316. DOI: 10.1523/JNEUROSCI.0874-15.2015.
  • Bakker R., Tiesinga P., Kötter R. (2015). The scalable brain atlas: instant web-based access to public brain atlases and related content. Neuroinformatics 13:353–366. DOI: 10.1007/s12021-014-9258-x.
  • Sergejeva M., Papp EA., Bakker R., Gaudnek MA., Okamura-Oho Y., Boline J., Bjaalie JG., Hess A. (2015). Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates. Journal of Neuroscience Methods 240:161–169. DOI: 10.1016/j.jneumeth.2014.11.005.
  • 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.
  • Milekovic T., Truccolo W., Grün S., Riehle A., Brochier T. (2015). Local field potentials in primate motor cortex encode grasp kinetic parameters. NeuroImage 114:338–355. DOI: 10.1016/j.neuroimage.2015.04.008.
  • 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.
  • Tiesinga P., Bakker R., Hill S., Bjaalie JG. (2015). Feeding the human brain model. Current Opinion in Neurobiology 32:107–114. DOI: 10.1016/j.conb.2015.02.003.
  • 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.
  • Zaytsev YV., Morrison A., Deger M. (2015). Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity. Journal of Computational Neuroscience 39:77–103. DOI: 10.1007/s10827-015-0565-5.
  • Bezgin G., Rybacki K., van Opstal AJ., Bakker R., Shen K., Vakorin VA., McIntosh AR., Kötter R. (2014). Auditory–prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams. Brain and Language 135:73–84. DOI: 10.1016/j.bandl.2014.05.006.
  • Chapuis A., Tetzlaff T. (2014). The variability of tidewater-glacier calving: origin of event-size and interval distributions. Journal of Glaciology 60:622–634. DOI: 10.3189/2014JoG13J215.
  • Cichon NB., Denker M., Grün S., Hanganu-Opatz IL. (2014). Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents. Frontiers in Neural Circuits 8:50. DOI: 10.3389/fncir.2014.00050.
  • 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.
  • Duarte RCF., Morrison A. (2014). Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience 8:124. DOI: 10.3389/fncom.2014.00124.
  • 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.
  • Ito J., Roy S., Liu Y., Cao Y., Fletcher M., Lu L., Boughter JD., Grün S., Heck DH. (2014). Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration. Nature Communications 5. DOI: 10.1038/ncomms4572.
  • Kriener B., Enger H., Tetzlaff T., Plesser HE., Gewaltig M-O., Einevoll GT. (2014). Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. Frontiers in Computational Neuroscience 8:136. DOI: 10.3389/fncom.2014.00136.
  • 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.
  • Pettersen KH., Lindén H., Tetzlaff T., Einevoll GT. (2014). Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the eeg. PLoS Computational Biology 10:e1003928. DOI: 10.1371/journal.pcbi.1003928.
  • 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.
  • Toledo-Suarez C., Duarte R., Morrison A. (2014). Liquid computing on and off the edge of chaos with a striatal microcircuit. Frontiers in Computational Neuroscience 8:130. DOI: 10.3389/fncom.2014.00130.
  • Zaytsev YV., Morrison A. (2014). CyNEST: a maintainable cython-based interface for the nest simulator. Frontiers in Neuroinformatics 8:23. DOI: 10.3389/fninf.2014.00023.
  • 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.
  • Heiberg T., Kriener B., Tetzlaff T., Casti A., Einevoll GT., Plesser HE. (2013). Firing-rate models capture essential response dynamics of lgn relay cells. Journal of Computational Neuroscience 35:359–375. DOI: 10.1007/s10827-013-0456-6.
  • 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.
  • Ito J., Maldonado P., Grün S. (2013). Cross-frequency interaction of the eye-movement related LFP signals in V1 of freely viewing monkeys. Frontiers in Systems Neuroscience 7:1. DOI: 10.3389/fnsys.2013.00001.
  • Kerr CC., van Albada SJ., Neymotin SA., Chadderdon GL., Robinson PA., Lytton WW. (2013). Cortical information flow in parkinson’s disease: a composite network/field model. Frontiers in Computational Neuroscience 7:39. DOI: 10.3389/fncom.2013.00039.
  • Łęski S., Lindén H., Tetzlaff T., Pettersen KH., Einevoll GT. (2013). Frequency dependence of signal power and spatial reach of the local field potential. PLoS Computational Biology 9:e1003137. DOI: 10.1371/journal.pcbi.1003137.
  • Nowke C, Hentschel B, Kuhlen T, Schmidt M, van Albada SJ, Eppler JM, Bakker R, Diesmann M. (2013) VisNEST – interactive analysis of neural activity data IEEE BioVis 65-72
  • Picado-Muiño D., Borgelt C., Berger D., Gerstein G., Grün S. (2013). Finding neural assemblies with frequent item set mining. Frontiers in Neuroinformatics 7:9. DOI: 10.3389/fninf.2013.00009.
  • Pipa G., Grün S., van Vreeswijk C. (2013). Impact of spike train autostructure on probability distribution of joint spike events. Neural Computation 25:1123–1163. DOI: 10.1162/NECO_a_00432.
  • Riehle A., Wirtssohn S., Grün S., Brochier T. (2013). Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements. Frontiers in Neural Circuits 7:48. DOI: 10.3389/fncir.2013.00048.
  • 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.
  • Torre E., Picado-Muiño D., Denker M., Borgelt C., Grün S. (2013). Statistical evaluation of synchronous spike patterns extracted by frequent item set mining. Frontiers in Computational Neuroscience 7:132. DOI: 10.3389/fncom.2013.00132.
  • 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.
  • van Albada SJ., Robinson PA. (2013). Relationships between electroencephalographic spectral peaks across frequency bands. Frontiers in Human Neuroscience 7:56. DOI: 10.3389/fnhum.2013.00056.
  • 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.
  • Yousaf M., Wyller J., Tetzlaff T., Einevoll GT. (2013). Effect of localized input on bump solutions in a two-population neural-field model. Nonlinear Analysis: Real World Applications 14:997–1025. DOI: 10.1016/j.nonrwa.2012.08.013.
  • Zaytsev YV., Morrison A. (2013). Increasing quality and managing complexity in neuroinformatics software development with continuous integration. Frontiers in Neuroinformatics 6:31. DOI: 10.3389/fninf.2012.00031.
  • 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.
  • Berger D., Pazienti A., Flores FJ., Nawrot MP., Maldonado PE., Grün S. (2012). Viewing strategy of cebus monkeys during free exploration of natural images. Brain Research 1434:34–46. DOI: 10.1016/j.brainres.2011.10.013.
  • Bezgin G., Vakorin VA., van Opstal AJ., McIntosh AR., Bakker R. (2012). Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain. NeuroImage 62:67–76. DOI: 10.1016/j.neuroimage.2012.04.013.
  • Borgelt C., Braune C., Kötter T., Grün S. (2012). New algorithms for finding approximate frequent item sets. Soft Computing 16:903–917. DOI: 10.1007/s00500-011-0776-2.
  • 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.
  • Shimazaki H., Amari S., Brown EN., Grün S. (2012). State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLoS Computational Biology 8:e1002385. DOI: 10.1371/journal.pcbi.1002385.
  • 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.
  • Chiang AKI., Rennie CJ., Robinson PA., van Albada SJ., Kerr CC. (2011). Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology 122:1505–1517. DOI: 10.1016/j.clinph.2011.01.040.
  • 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.
  • 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.
  • Ito J., Maldonado P., Singer W., Grün S. (2011). Saccade-related modulations of neuronal excitability support synchrony of visually elicited spikes. Cerebral Cortex 21:2482–2497. DOI: 10.1093/cercor/bhr020.
  • 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.
  • Berger D., Borgelt C., Louis S., Morrison A., Grün S. (2010). Efficient identification of assembly neurons within massively parallel spike trains. Computational Intelligence and Neuroscience 2010:1–18. DOI: 10.1155/2010/439648.
  • 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.
  • Denker M., Finke R., Schaupp F., Grün S., Menzel R. (2010b). Neural correlates of odor learning in the honeybee antennal lobe. European Journal of Neuroscience 31:119–133. DOI: 10.1111/j.1460-9568.2009.07046.x.
  • Denker M., Riehle A., Diesmann M., Grün S. (2010a). 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 S., Borgelt C., Grün S. (2010). Complexity distribution as a measure for assembly size and temporal precision. Neural Networks 23:705–712. DOI: 10.1016/j.neunet.2010.05.004.
  • 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.
  • Staude B., Rotter S. (2010). Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference. Frontiers in Computational Neuroscience 4:16. DOI: 10.3389/fncom.2010.00016.
  • Staude B., Rotter S., Grün S. (2010). CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains. Journal of Computational Neuroscience 29:327–350. DOI: 10.1007/s10827-009-0195-x.
  • Grün S. (2009). Data-driven significance estimation for precise spike correlation. Journal of Neurophysiology 101:1126–1140. DOI: 10.1152/jn.00093.2008.
  • Kilavik BE., Roux S., Ponce-Alvarez A., Confais J., Grün S., Riehle A. (2009). Long-term modifications in motor cortical dynamics induced by intensive practice. Journal of Neuroscience 29:12653–12663. DOI: 10.1523/JNEUROSCI.1554-09.2009.
  • 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.
  • Nordlie E., Gewaltig M-O., Plesser HE. (2009). Towards reproducible descriptions of neuronal network models. PLoS Computational Biology 5:e1000456. DOI: 10.1371/journal.pcbi.1000456.
  • Plesser HE., Diesmann M. (2009). Simplicity and efficiency of integrate-and-fire neuron models. Neural Computation 21:353–359. DOI: 10.1162/neco.2008.03-08-731.
  • Potjans W., Morrison A., Diesmann M. (2009). A spiking neural network model of an actor-critic learning agent. Neural Computation 21:301–339. DOI: 10.1162/neco.2008.08-07-593.
  • Sharott A., Moll CKE., Engler G., Denker M., Grün S., Engel AK. (2009). Different subtypes of striatal neurons are selectively modulated by cortical oscillations. Journal of Neuroscience 29:4571–4585. DOI: 10.1523/JNEUROSCI.5097-08.2009.
  • 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.
  • 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.
  • 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.
  • Maldonado P., Babul C., Singer W., Rodriguez E., Berger D., Grün S. (2008). Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. Journal of Neurophysiology 100:1523–1532. DOI: 10.1152/jn.00076.2008.
  • 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., Grün 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.
  • Staude B., Rotter S., Grün S. (2008). Can spike coordination be differentiated from rate covariation? Neural Computation 20:1973–1999. DOI: 10.1162/neco.2008.06-07-550.
  • 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.
  • Berger D., Warren D., Normann R., Arieli A., Grün S. (2007). Spatially organized spike correlation in cat visual cortex. Neurocomputing 70:2112–2116. DOI: 10.1016/j.neucom.2006.10.141.
  • Denker M., Roux S., Timme M., Riehle A., Grün S. (2007). Phase synchronization between lfp and spiking activity in motor cortex during movement preparation. Neurocomputing 70:2096–2101. DOI: 10.1016/j.neucom.2006.10.088.
  • 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.
  • Nawrot MP., Boucsein C., Rodriguez-Molina V., Aertsen A., Grün S., Rotter S. (2007). Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro. Neurocomputing 70:1717–1722. DOI: 10.1016/j.neucom.2006.10.101.
  • Pipa G., Riehle A., Grün S. (2007). Validation of task-related excess of spike coincidences based on neuroxidence. Neurocomputing 70:2064–2068. DOI: 10.1016/j.neucom.2006.10.142.
  • 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.
  • Pazienti A., Grün S. (2006). Robustness of the significance of spike synchrony with respect to sorting errors. Journal of Computational Neuroscience 21:329–342. DOI: 10.1007/s10827-006-8899-7.
  • 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.
  • Czanner G., Grün S., Iyengar S. (2005). Theory of the snowflake plot and its relations to higher-order analysis methods. Neural Computation 17:1456–1479. DOI: 10.1162/0899766053723041.
  • 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.
  • Helias M., Pfannkuche D. (2004). Tunneling of quasiholes in the fractional quantum hall regime. Diploma Thesis. arXiv cond-mat/0403126
  • 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.
  • Gál V., Grün S., Tetzlaff R. (2003). Analysis of multidimensional neural activity via CNN-UM. International Journal of Neural Systems 13:479–487. DOI: 10.1142/S0129065703001789.
  • 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.
  • Pipa G., Grün S. (2003). Non-parametric significance estimation of joint-spike events by shuffling and resampling. Neurocomputing 52–54:31–37. DOI: 10.1016/S0925-2312(02)00823-8.
  • Schneider G., Grün S. (2003). Analysis of higher-order correlations in multiple parallel processes. Neurocomputing 52–54:771–777. DOI: 10.1016/S0925-2312(02)00772-5.
  • 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.
  • Martignon L., Von Hassein H., Grün S., Aertsen A., Palm G. (1995). Detecting higher-order interactions among the spiking events in a group of neurons. Biological Cybernetics 73:69–81. DOI: 10.1007/BF00199057.