Search

link to homepage

Institute of Neuroscience and Medicine
Computational and Systems Neuroscience (INM-6 / IAS-6)
(leer)

Navigation and service

Go to:

Peer-reviewed articles of Computation in Neural Circuits

2020
  • Bachmann C., Tetzlaff T., Duarte R., Morrison A. (2020) Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease PLoS Computational Biology 16(8) DOI:10.1371/journal.pcbi.1007790
2019
  • Duarte R, Morrison A. (2019) Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLoS Comput Biol. 25;15(4):e1006781. DOI:10.1371/journal.pcbi.1006781.
  • Jordan J., Weidel P., Morrison A. (2019) A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. Front. Comput. Neurosci. 13:46. DOI:10.3389/fncom.2019.00046
  • Zajzon B., Morales-Gregorio A. (2019) Trans-thalamic Pathways: Strong Candidates for Supporting Communication between Functionally Distinct Cortical Areas Journal of Neuroscience 4 September 2019, 39 (36) 7034-7036 DOI:10.1523/JNEUROSCI.0656-19.2019
2018
  • Bachmann C., Jacobs HIL., Porta Mana P., Dillen K., Richter N., von Reutern B., Dronse J., Onur O. A., Langen K.-J., Fink G. R., Kukolja J., Morrison, A. (2018) On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease Frontiers in neuroscience 12, 528 DOI:10.3389/fnins.2018.00528
  • Bahuguna J., Weidel P., Morrison A. (2018) Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework European journal of neuroscience 0, 1-41 DOI:10.1111/ejn.14021
  • Blundell I., Brette R., Cleland TA., Close TG., Coca D., Davison AP., Diaz S., Fernandez Musoles C., Gleeson P., Goodman DFM., Hines M., Hopkin, MW., Kumbhar P., Lester DR., Marin B., Morrison A., Müller E., Nowotny T., Peyser A., Plotnikov D., Richmond P., Rowley A., Rumpe B., Stimberg M., Stokes AB., Tomkins A., Trensch G., Woodman M., Eppler JM.(2018) Code Generation in Computational Neuroscience: A Review of Tools and Techniques Frontiers in neuroinformatics 12, 68 ,DOI:10.3389/fninf.2018.00068
  • Blundell I., Plotnikov D., Eppler JM., Morrison A. (2018) Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models Frontiers in neuroinformatics 12, 50 ,DOI:10.3389/fninf.2018.00050
  • Heiberg T., Kriener B., Tetzlaff T., Einevoll GT., Plesser HE. (2018) Firing-rate models for neurons with a broad repertoire of spiking behaviors Journal of computational neuroscience 45(2), 103-132 DOI:10.1007/s10827-018-0693-9
  • Nowke C, Diaz-Pier S, Weyers B, Hentschel B, Morrison A, Kuhlen TW, Peyser A. (2018) Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation. Front Neuroinform. 2018 Jun 1;12:32. DOI:10.3389/fninf.2018.00032.
  • Pauli R., Weidel P., Kunkel S., Morrison, A. (2018) Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models Frontiers in neuroinformatics 12, 46 DOI:10.3389/fninf.2018.00046
  • Trensch G., Gutzen R., Blundell I., Denker M., Morrison A. (2018) Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data Frontiers in neuroinformatics 12, 81 DOI:10.3389/fninf.2018.00081 special issue: "Reproducibility and Rigour in Computational Neuroscience"
2017
  • 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.
  • 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.
  • 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.
2016
  • 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.
  • 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.
  • 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.
  • 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.
2015
  • 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.
  • 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.
2014
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
2013
  • 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.
  • 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.
  • 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.
2012
  • 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.
  • Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A, Diesmann M. (2012) Meeting the memory challenges of brain-scale network simulation. Front Neuroinform. 2012 Jan 24;5:35. DOI:10.3389/fninf.2011.00035.
2011
  • Hanuschkin A, Diesmann M, Morrison A. (2011) A reafferent and feed-forward model of song syntax generation in the Bengalese finch. J Comput Neurosci. 2011 Nov;31(3):509-32. 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. J Comput Neurosci. 2011 Jun;30(3):675-97. DOI:10.1007/s10827-010-0285-9.
  • 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 Comput Biol. 2011 May;7(5):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.
2010
  • 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.
  • 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 Front. Neuroinform. 4:113. DOI:10.3389/fninf.2010.00113
  • 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.
  • Potjans W., Morrison A., Diesmann M. (2010). Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Front. Comput. Neurosci. 4:141. DOI:10.3389/fncom.2010.00141
2009 2008
  • Morrison A., Diesmann M., Gerstner W. (2008) Phenomenological Models of Synaptic Plasticity based on Spike-Timing Biological Cybernetics 98(6):459-478. DOI:10.1007/s00422-008-0233-1
2007
  • Morrison A., Aertsen A., Diesmann M. (2007) Spike-time dependent plasticity in balanced random networks Neural Computation 19:1437-1467. DOI:10.1162/neco.2007.19.6.1437 in 2008 among top downloads from NECO
  • 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
  • Plesser H E., Eppler J M., Morrison A., Diesmann M., Gewaltig M-O. (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers Euro-Par 2007, Proceedings of the 13th International Euro-Par Conference, LCNS Springer 4641: 672-681. DOI:10.1007/978-3-540-74466-5_71
  • Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower JM., Diesmann M., Morrison A., Goodman PH., Harris FC Jr., Zirpe M., Natschläger T., Pecevski D., Ermentrout B., Djurfeldt M., Lasner 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(3): 349-398. DOI:10.1007/s10827-007-0038-6
2006
  • Guerrero-Rivera R., Morrison A., Diesmann M., Pearce T C. (2006) Programmable Logic Construction Kits for Hyper Real-time Neuronal Modeling Neural Computation 18:2651--2679. DOI:10.1162/neco.2006.18.11.2651
2005
  • 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(8):1776--1801. DOI:10.1162/0899766054026648
2004
  • 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

Servicemeu

Homepage

institutes

Administration