Peer-reviewed publications


  • Golosio B., Villamar J., Tiddia G., Pastorelli E., Stapmanns J., Fanti V., Paolucci PS., Morrison A., Senk J. (2023) Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices. Applied Sciences 13(17):9598.
    DOI: 10.3390/app13179598

  • Schulte to Brinke T., Dick M., Duarte R., Morrison A. (2023) A refined information processing capacity metric allows an in-depth analysis of memory and nonlinearity trade-offs in neurocomputational systems. Scientific Reports 2023 Jun 29;13(1):10517.
    DOI: 10.1038/s41598-023-37604-0

  • Wybo WAM., Tsai MC., Tran VAK., Illing B., Jordan J., Morrison A., Senn W. (2023) NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways Proceedings of the National Academy of Sciences 120(32):e2300558120.
    DOI: 10.1073/pnas.2300558120

  • Zajzon B., Dahmen D., Morrison A., Duarte R. (2023) Signal denoising through topographic modularity of neural circuits. eLife 12:e77009.
    DOI: 10.7554/eLife.77009

  • Zajzon B., Duarte R., Morrison, A. (2023) Towards reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning. Frontiers in Integrative Neuroscience, 17. Accepted.


  • Feldotto B., Eppler JM., Jimenez-Romero C., Bignamini C., Gutierrez CE., Albanese U., Retamino E., Vorobev V., Zolfaghari V., Upton A., Sun Z., Yamaura H., Heidarinejad M., Klijn W., Morrison A., Cruz F., McMurtrie C., Knoll AC., Igarashi J., Yamazaki T., Doya K., Morin FO. (2022) Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure. Frontiers in Neuroinformatics 16:884180.
    DOI: 10.3389/fninf.2022.884180

  • Hagen E., Magnusson SH., Ness TV., Halnes G., Babu PN., Linssen C., Morrison A., Einevoll GT. (2022) Brain signal predictions from multi-scale networks using a linearized framework. PLoS Computational Biology 18(8):e1010353
    DOI: 10.1371/journal.pcbi.1010353

  • Herbers P., Calvo I., Diaz-Pier S., Robles OD., Mata S., Toharia P., Pastor L., Peyser A., Morrison A., Klijn W. (2022) ConGen - A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks. Frontiers in Neuroinformatics 15:766697.
    DOI: 10.3389/fninf.2021.766697

  • Oberländer J., Bouhadjar Y., Morrison A. (2022) Learning and replaying spatiotemporal sequences: A replication study. Frontiers in Integrative Neuroscience 16:974177.
    DOI: 10.3389/fnint.2022.974177

  • Schulte to Brinke T., Duarte R. Morrison A. (2022) Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model. Frontiers in Integrative Neuroscience 16:923468.

  • Trensch G., Morrison A. (2022) A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks. Frontiers in Neuroinformatics 16:884033.
    DOI: 10.3389/fninf.2022.884033

  • van der Vlag M., Woodman M., Fousek J., Diaz-Pier S., Pérez Martín A., Jirsa  V., Morrison A. (2022) RateML: A Code Generation Tool for Brain Network Models. Frontiers in Network Physiology 2:826345.
    DOI: 10.3389/fnetp.2022.826345

  • Yegenoglu A., Subramoney A., Hater T., Jimenez-Romero C., Klijn W., Pérez Martín A., van der Vlag M., Herty M., Morrison A., Diaz S. (2022) Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. Frontiers in Computational Neuroscience 16:885207
    DOI: 10.3389/fncom.2022.885207


  • Weidel P., Duarte R., Morrison A. (2021) Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks Frontiers in Computational Neuroscience 15:543872.
    DOI: 10.3389/fncom.2021.543872


  • 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


  • Duarte R., Morrison A. (2019) Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLoS Computational Biology 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. Frontiers in Computational Neuroscience 13:46.
    DOI: 10.3389/fncom.2019.00046

  • Peyser A., Diaz Pier S., Klijn W., Morrison A., Triesch J. (2019) Editorial: Linking experimental and computational connectomics. Network Neuroscience 3(4):902-904.
    DOI: 10.1162/netn_e_00108

  • Zajzon B., Mahmoudian S., Morrison A., Duarte R. (2019) Passing the Message: Representation Transfer in Modular Balanced Networks. Frontiers in Computational Neuroscience 13:79.
    DOI: 10.3389/fncom.2019.00079

  • Zajzon B., Morales-Gregorio A. (2019) Trans-thalamic Pathways: Strong Candidates for Supporting Communication between Functionally Distinct Cortical Areas. Journal of Neuroscience 39:7034-7036.
    DOI: 10.1523/JNEUROSCI.0656-19.2019


  • Bachmann C., Jacobs HIL., Porta Mana P., Dillen K., Richter N., von Reutern B., Dronse J., Onur OA., Langen KJ., Fink GR., 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 49:737-753.
    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 45:103-132.
    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. Frontiers in Neuroinformatics 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


  • 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 4(4).
    DOI: 10.1523/ENEURO.0348-16.2017.


  • 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 6:021023.
    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.


  • 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.


  • 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 R., 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 MO., 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.


  • 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


  • 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.

  • 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.


  • 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.

  • 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.


  • 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. Frontiers in Neuroinformatics 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. Frontiers in Computational Neuroscience 4:141.
    DOI: 10.3389/fncom.2010.00141.


  • 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.


  • 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.


  • 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

  • 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.

  • Plesser HE., Eppler JM., Morrison A., Diesmann M., Gewaltig MO. (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


  • 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.


  • 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.
Last Modified: 06.09.2023