Peer-reviewed publications

2025

• Linssen C., Babu PN., Eppler JM., Koll L., Rumpe B., Morrison A. (2025) NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules. Front. Neuroinform. 19:1544143. DOI: 10.3389/fninf.2025.1544143
  
  

2024

• Dick M., van Meegen A., Helias M. (2024) Linking network- and neuron-level correlations by renormalized field theory. Physical Review Research 6, 033264
  DOI: 10.1103/PhysRevResearch.6.033264
  
  
• Jiang H-J., Qi G., Duarte R., Feldmeyer D., van Albada SJ. (2024) A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity. Cerebral Cortex 34 (9), bhae378
  DOI: 10.1093/cercor/bhae378
  
  
• Kipp J., Lux FR., Pürling T., Morrison A., Blügel S., Pinna D., Mokrousov Y. (2024) Machine learning inspired models for Hall effects in non-collinear magnets. Machine learning: science and technology 5(2):025060
  DOI: 10.1088/2632-2153/ad51ca
  
  
• Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A and Jirsa V (2024) Multiscale co-simulation design pattern for neuroscience applications. Front. Neuroinform. 18:1156683.
  DOI: 10.3389/fninf.2024.1156683
  
  

2023

• 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
  
  
• Quercia A., Morrison A., Scharr H., Assent I. (2023) SGD Biased towards Early Important Samples for Efficient Training. IEEE International Conference on Data Mining (ICDM), Shanghai, China, 2023, 1289-1294,
  DOI: 10.1109/ICDM58522.2023.00163
  
  
• 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.
  DOI: 10.3389/fnint.2023.935177
  
  

2022

• 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.
  DOI: 10.3389/fnint.2022.923468
  
  
• 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
  
  

2021

• 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
  
  

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):e1007790.
  DOI: 10.1371/journal.pcbi.1007790
  
  
• Fitz H., Uhlmann M., van den Broek D., Duarte R., Hagoort P., Petersson, K. M. (2020) Neuronal spike-rate adaptation supports working memory in language processing. Proceedings of the National Academy of Sciences of the United States of America 117(34): 20881-20889.
  DOI: 10.1073/pnas.2000222117
  
  

2019

• 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 (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 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
  
  
• 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
  
  

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 4(4).
  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.
  
  
• 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.
  
  
• 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

• van Albada SJ., Kunkel S., Morrison A., Diesmann M. (2014) Integrating Brain Structure and Dynamics on Supercomputers. In: Grandinetti L., Lippert T., Petkov N. eds. Brain-Inspired Computing LNCS 8603:22-32.
  DOI: 10.1007/978-3-319-12084-3_3
  
  
• 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.
  
  
• 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.
  
  
• 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

• 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.
  
  
• 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.
  
  

2011

• 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.
  
  
• 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.
  
  

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. 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.
  
  

2009

• 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.
  
  

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.
  
  

2007

• 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
  
  

2006

• 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.
  
  

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 17:1776–1801.
  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.