Publications

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Citation Statistics

Uses PD14

Publications that use the PD14 microcircuit model.

• Giulia Moreni, Licheng Zou, Cyriel M. A. Pennartz, and Jorge F. Mejias. Synaptic plasticity facilitates oscillations in a v1 cortical column model with multiple interneuron types. Frontiers in Computational Neuroscience, April 2025. URL: http://dx.doi.org/10.3389/fncom.2025.1568143, doi:10.3389/fncom.2025.1568143.

• José Villamar, Matthias Kelbling, Heather L. More, Michael Denker, Tom Tetzlaff, Johanna Senk, and Stephan Thober. Metadata practices for simulation workflows. Scientific Data, June 2025. URL: http://dx.doi.org/10.1038/s41597-025-05126-1, doi:10.1038/s41597-025-05126-1.

• Robin Kim, Yuxuan Liu, Jiaao Zhang, Chong Xie, and Lan Luan. Towards precise synthetic neural codes: high-dimensional stimulation with flexible electrodes. npj Flexible Electronics, 07 2025. URL: https://doi.org/10.1038/s41528-025-00447-y, doi:10.1038/s41528-025-00447-y.

• Cecilia Romaro, Jose Roberto Castilho Piqueira, and A. C. Roque. Adding space to random networks of spiking neurons: a method based on scaling the network size. Neural Computation, 37(5):957–986, April 2025. URL: http://dx.doi.org/10.1162/neco_a_01747, doi:10.1162/neco_a_01747.

• Han-Jia Jiang, Guanxiao Qi, Renato Duarte, Dirk Feldmeyer, and Sacha J van Albada. A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity. Cerebral Cortex, September 2024. URL: http://dx.doi.org/10.1093/cercor/bhae378, doi:10.1093/cercor/bhae378.

• Jari Pronold, Alexander van Meegen, Renan O Shimoura, Hannah Vollenbröker, Mario Senden, Claus C Hilgetag, Rembrandt Bakker, and Sacha J van Albada. Multi-scale spiking network model of human cerebral cortex. Cerebral Cortex, October 2024. URL: http://dx.doi.org/10.1093/cercor/bhae409, doi:10.1093/cercor/bhae409.

• Markus Robens, Robert Kleijnen, Michael Schiek, and Stefan van Waasen. Noc simulation steered by nest: mcaersim and a noxim patch. Frontiers in Neuroscience, June 2024. URL: http://dx.doi.org/10.3389/fnins.2024.1371103, doi:10.3389/fnins.2024.1371103.

• Felix Wang, Shruti Kulkarni, Bradley Theilman, Fredrick Rothganger, Catherine Schuman, Seung-Hwan Lim, and James B Aimone. Scaling neural simulations in stacs. Neuromorphic Computing and Engineering, 4(2):024002, April 2024. URL: http://dx.doi.org/10.1088/2634-4386/ad3be7, doi:10.1088/2634-4386/ad3be7.

• Jari Pronold, Aitor Morales-Gregorio, Vahid Rostami, and Sacha J. van Albada. Cortical multi-area model with joint excitatory-inhibitory clusters accounts for spiking statistics, inter-area propagation, and variability dynamics. bioRxiv, January 2024. URL: http://dx.doi.org/10.1101/2024.01.30.577979, doi:10.1101/2024.01.30.577979.

• Johanna Senk, Espen Hagen, Sacha J. van Albada, and Markus Diesmann. Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space. Cerebral Cortex, 09 2024. URL: https://doi.org/10.1093/cercor/bhae405, doi:10.1093/cercor/bhae405.

• Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li, and Si Wu. Brainpy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife, December 2023. URL: http://dx.doi.org/10.7554/elife.86365, doi:10.7554/elife.86365.

• Kevin Kauth, Tim Stadtmann, Vida Sobhani, and Tobias Gemmeke. Neuroaix: fpga cluster for reproducible and accelerated neuroscience simulations of snns. In 2023 IEEE Nordic Circuits and Systems Conference (NorCAS), 1–7. IEEE, October 2023. URL: http://dx.doi.org/10.1109/norcas58970.2023.10305473, doi:10.1109/norcas58970.2023.10305473.

• Mariana Bergonzi, Joaquín Fernández, Rodrigo Castro, Alexandre Muzy, and Ernesto Kofman. Quantization-based simulation of spiking neurons: theoretical properties and performance analysis. Journal of Simulation, 18(5):789–812, November 2023. URL: http://dx.doi.org/10.1080/17477778.2023.2284143, doi:10.1080/17477778.2023.2284143.

• Kevin Kauth, Tim Stadtmann, Vida Sobhani, and Tobias Gemmeke. Neuroaix-framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time. Frontiers in Computational Neuroscience, April 2023. URL: http://dx.doi.org/10.3389/fncom.2023.1144143, doi:10.3389/fncom.2023.1144143.

• Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison, and Johanna Senk. Runtime construction of large-scale spiking neuronal network models on gpu devices. Applied Sciences, 13(17):9598, August 2023. URL: http://dx.doi.org/10.3390/app13179598, doi:10.3390/app13179598.

• Patrick Herbers, Iago Calvo, Sandra Diaz-Pier, Oscar D. Robles, Susana Mata, Pablo Toharia, Luis Pastor, Alexander Peyser, Abigail Morrison, and Wouter Klijn. Congen—a simulator-agnostic visual language for definition and generation of connectivity in large and multiscale neural networks. Frontiers in Neuroinformatics, January 2022. URL: http://dx.doi.org/10.3389/fninf.2021.766697, doi:10.3389/fninf.2021.766697.

• Anno C Kurth, Johanna Senk, Dennis Terhorst, Justin Finnerty, and Markus Diesmann. Sub-realtime simulation of a neuronal network of natural density. Neuromorphic Computing and Engineering, 2(2):021001, March 2022. URL: http://dx.doi.org/10.1088/2634-4386/ac55fc, doi:10.1088/2634-4386/ac55fc.

• Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, and Johanna Senk. A modular workflow for performance benchmarking of neuronal network simulations. Frontiers in Neuroinformatics, May 2022. URL: http://dx.doi.org/10.3389/fninf.2022.837549, doi:10.3389/fninf.2022.837549.

• Arne Heittmann, Georgia Psychou, Guido Trensch, Charles E. Cox, Winfried W. Wilcke, Markus Diesmann, and Tobias G. Noll. Simulating the cortical microcircuit significantly faster than real time on the ibm inc-3000 neural supercomputer. Frontiers in Neuroscience, January 2022. URL: http://dx.doi.org/10.3389/fnins.2021.728460, doi:10.3389/fnins.2021.728460.

• Hugh Osborne, Lukas Deutz, and Marc de Kamps. Multidimensional Dynamical Systems with Noise: Population Density Techniques for Neuroscience, pages 159–178. Springer International Publishing, October 2021. URL: http://dx.doi.org/10.1007/978-3-030-89439-9_7, doi:10.1007/978-3-030-89439-9_7.

• Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, and Johanna Senk. Dynamical characteristics of recurrent neuronal networks are robust against low synaptic weight resolution. Frontiers in Neuroscience, December 2021. URL: http://dx.doi.org/10.3389/fnins.2021.757790, doi:10.3389/fnins.2021.757790.

• Hugh Osborne, Yi Ming Lai, Mikkel Elle Lepperød, David Sichau, Lukas Deutz, and Marc de Kamps. Miind : a model-agnostic simulator of neural populations. Frontiers in Neuroinformatics, July 2021. URL: http://dx.doi.org/10.3389/fninf.2021.614881, doi:10.3389/fninf.2021.614881.

• Renan Oliveira Shimoura, Rodrigo F. O. Pena, Vinicius Lima, Nilton L. Kamiji, Mauricio Girardi-Schappo, and Antonio C. Roque. Building a model of the brain: from detailed connectivity maps to network organization. The European Physical Journal Special Topics, 230(14–15):2887–2909, June 2021. URL: http://dx.doi.org/10.1140/epjs/s11734-021-00152-7, doi:10.1140/epjs/s11734-021-00152-7.

• Cecilia Romaro, Fernando Araujo Najman, William W. Lytton, Antonio C. Roque, and Salvador Dura-Bernal. Netpyne implementation and scaling of the potjans-diesmann cortical microcircuit model. Neural Computation, 33(7):1993–2032, June 2021. URL: http://dx.doi.org/10.1162/neco_a_01400, doi:10.1162/neco_a_01400.

• James C. Knight, Anton Komissarov, and Thomas Nowotny. Pygenn: a python library for gpu-enhanced neural networks. Frontiers in Neuroinformatics, April 2021. URL: http://dx.doi.org/10.3389/fninf.2021.659005, doi:10.3389/fninf.2021.659005.

• Giuseppe Giacopelli, Domenico Tegolo, Emiliano Spera, and Michele Migliore. On the structural connectivity of large-scale models of brain networks at cellular level. Scientific Reports, February 2021. URL: http://dx.doi.org/10.1038/s41598-021-83759-z, doi:10.1038/s41598-021-83759-z.

• Bruno Golosio, Gianmarco Tiddia, Chiara De Luca, Elena Pastorelli, Francesco Simula, and Pier Stanislao Paolucci. Fast simulations of highly-connected spiking cortical models using gpus. Frontiers in Computational Neuroscience, February 2021. URL: http://dx.doi.org/10.3389/fncom.2021.627620, doi:10.3389/fncom.2021.627620.

• Solveig Næss, Geir Halnes, Espen Hagen, Donald J. Hagler, Anders M. Dale, Gaute T. Einevoll, and Torbjørn V. Ness. Biophysically detailed forward modeling of the neural origin of eeg and meg signals. NeuroImage, 225:117467, January 2021. URL: http://dx.doi.org/10.1016/j.neuroimage.2020.117467, doi:10.1016/j.neuroimage.2020.117467.

• Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, David S Greenberg, and Jakob H Macke. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, September 2020. URL: http://dx.doi.org/10.7554/elife.56261, doi:10.7554/elife.56261.

• Alexandre René, André Longtin, and Jakob H. Macke. Inference of a mesoscopic population model from population spike trains. Neural Computation, 32(8):1448–1498, August 2020. URL: http://dx.doi.org/10.1162/neco_a_01292, doi:10.1162/neco_a_01292.

• Oliver Rhodes, Luca Peres, Andrew G. D. Rowley, Andrew Gait, Luis A. Plana, Christian Brenninkmeijer, and Steve B. Furber. Real-time cortical simulation on neuromorphic hardware. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378(2164):20190160, December 2019. URL: http://dx.doi.org/10.1098/rsta.2019.0160, doi:10.1098/rsta.2019.0160.

• Christian L. Ebbesen, Evgeny Bobrov, Rajnish P. Rao, and Michael Brecht. Highly structured, partner-sex- and subject-sex-dependent cortical responses during social facial touch. Nature Communications, October 2019. URL: http://dx.doi.org/10.1038/s41467-019-12511-z, doi:10.1038/s41467-019-12511-z.

• Benjamin Merkt, Friedrich Schüßler, and Stefan Rotter. Propagation of orientation selectivity in a spiking network model of layered primary visual cortex. PLOS Computational Biology, 15(7):e1007080, July 2019. URL: http://dx.doi.org/10.1371/journal.pcbi.1007080, doi:10.1371/journal.pcbi.1007080.

• Padraig Gleeson, Matteo Cantarelli, Boris Marin, Adrian Quintana, Matt Earnshaw, Sadra Sadeh, Eugenio Piasini, Justas Birgiolas, Robert C. Cannon, N. Alex Cayco-Gajic, Sharon Crook, Andrew P. Davison, Salvador Dura-Bernal, András Ecker, Michael L. Hines, Giovanni Idili, Frederic Lanore, Stephen D. Larson, William W. Lytton, Amitava Majumdar, Robert A. McDougal, Subhashini Sivagnanam, Sergio Solinas, Rokas Stanislovas, Sacha J. van Albada, Werner van Geit, and R. Angus Silver. Open source brain: a collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron, 103(3):395–411.e5, August 2019. URL: http://dx.doi.org/10.1016/j.neuron.2019.05.019, doi:10.1016/j.neuron.2019.05.019.

• Salvador Dura-Bernal, Benjamin A Suter, Padraig Gleeson, Matteo Cantarelli, Adrian Quintana, Facundo Rodriguez, David J Kedziora, George L Chadderdon, Cliff C Kerr, Samuel A Neymotin, Robert A McDougal, Michael Hines, Gordon MG Shepherd, and William W Lytton. Netpyne, a tool for data-driven multiscale modeling of brain circuits. eLife, April 2019. URL: http://dx.doi.org/10.7554/elife.44494, doi:10.7554/elife.44494.

• Carlos Fernandez-Musoles, Daniel Coca, and Paul Richmond. Communication sparsity in distributed spiking neural network simulations to improve scalability. Frontiers in Neuroinformatics, April 2019. URL: http://dx.doi.org/10.3389/fninf.2019.00019, doi:10.3389/fninf.2019.00019.

• Andrew G. D. Rowley, Christian Brenninkmeijer, Simon Davidson, Donal Fellows, Andrew Gait, David R. Lester, Luis A. Plana, Oliver Rhodes, Alan B. Stokes, and Steve B. Furber. Spinntools: the execution engine for the spinnaker platform. Frontiers in Neuroscience, March 2019. URL: http://dx.doi.org/10.3389/fnins.2019.00231, doi:10.3389/fnins.2019.00231.

• Francesco Barchi, Gianvito Urgese, Alessandro Siino, Santa Di Cataldo, Enrico Macii, and Andrea Acquaviva. Flexible on-line reconfiguration of multi-core neuromorphic platforms. IEEE Transactions on Emerging Topics in Computing, 9:915–927, 03 2019. URL: https://doi.org/10.1109/tetc.2019.2908079, doi:10.1109/tetc.2019.2908079.

• James C. Knight and Thomas Nowotny. Gpus outperform current hpc and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Frontiers in Neuroscience, December 2018. URL: http://dx.doi.org/10.3389/fnins.2018.00941, doi:10.3389/fnins.2018.00941.

• Christian Nowke, Sandra Diaz-Pier, Benjamin Weyers, Bernd Hentschel, Abigail Morrison, Torsten W. Kuhlen, and Alexander Peyser. Toward rigorous parameterization of underconstrained neural network models through interactive visualization and steering of connectivity generation. Frontiers in Neuroinformatics, June 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00032, doi:10.3389/fninf.2018.00032.

• Martin Völker, Lukas D.J. Fiederer, Sofie Berberich, Jiří Hammer, Joos Behncke, Pavel Kršek, Martin Tomášek, Petr Marusič, Peter C. Reinacher, Volker A. Coenen, Moritz Helias, Andreas Schulze-Bonhage, Wolfram Burgard, and Tonio Ball. The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial eeg. NeuroImage, 173:564–579, June 2018. URL: http://dx.doi.org/10.1016/j.neuroimage.2018.01.059, doi:10.1016/j.neuroimage.2018.01.059.

• Sacha J. van Albada, Andrew G. Rowley, Johanna Senk, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Markus Diesmann, and Steve B. Furber. 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, May 2018. URL: http://dx.doi.org/10.3389/fnins.2018.00291, doi:10.3389/fnins.2018.00291.

• Gianvito Urgese, Francesco Barchi, Enrico Macii, and Andrea Acquaviva. Optimizing network traffic for spiking neural network simulations on densely interconnected many-core neuromorphic platforms. IEEE Transactions on Emerging Topics in Computing, 6(3):317–329, July 2018. URL: http://dx.doi.org/10.1109/tetc.2016.2579605, doi:10.1109/tetc.2016.2579605.

• Maximilian Schmidt, Rembrandt Bakker, Claus C. Hilgetag, Markus Diesmann, and Sacha J. van Albada. Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function, 223(3):1409–1435, November 2017. URL: http://dx.doi.org/10.1007/s00429-017-1554-4, doi:10.1007/s00429-017-1554-4.

• Clément Vitrac and Marianne Benoit-Marand. Monoaminergic modulation of motor cortex function. Frontiers in Neural Circuits, October 2017. URL: http://dx.doi.org/10.3389/fncir.2017.00072, doi:10.3389/fncir.2017.00072.

• Tilo Schwalger, Moritz Deger, and Wulfram Gerstner. Towards a theory of cortical columns: from spiking neurons to interacting neural populations of finite size. PLOS Computational Biology, 13(4):e1005507, April 2017. URL: http://dx.doi.org/10.1371/journal.pcbi.1005507, doi:10.1371/journal.pcbi.1005507.

• Jannis Schuecker, Maximilian Schmidt, Sacha J. van Albada, Markus Diesmann, and Moritz Helias. Fundamental activity constraints lead to specific interpretations of the connectome. PLOS Computational Biology, 13(2):e1005179, February 2017. URL: http://dx.doi.org/10.1371/journal.pcbi.1005179, doi:10.1371/journal.pcbi.1005179.

• Jung Hoon Lee and Stefan Mihalas. Visual processing mode switching regulated by vip cells. Scientific Reports, May 2017. URL: http://dx.doi.org/10.1038/s41598-017-01830-0, doi:10.1038/s41598-017-01830-0.

• Jung H. Lee, Christof Koch, and Stefan Mihalas. A computational analysis of the function of three inhibitory cell types in contextual visual processing. Frontiers in Computational Neuroscience, April 2017. URL: http://dx.doi.org/10.3389/fncom.2017.00028, doi:10.3389/fncom.2017.00028.

• Tammo Ippen, Jochen M. Eppler, Hans E. Plesser, and Markus Diesmann. Constructing neuronal network models in massively parallel environments. Frontiers in Neuroinformatics, May 2017. URL: http://dx.doi.org/10.3389/fninf.2017.00030, doi:10.3389/fninf.2017.00030.

• Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias Bolten, Moritz Helias, and Markus Diesmann. Integration of continuous-time dynamics in a spiking neural network simulator. Frontiers in Neuroinformatics, May 2017. URL: http://dx.doi.org/10.3389/fninf.2017.00034, doi:10.3389/fninf.2017.00034.

• Alessandro Siino, Francesco Barchi, Sergio Davies, Gianvito Urgese, and Andrea Acquaviva. Data and commands communication protocol for neuromorphic platform configuration. In 2016 IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC), 23–30. IEEE, September 2016. URL: http://dx.doi.org/10.1109/MCSoC.2016.41, doi:10.1109/mcsoc.2016.41.

• Espen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Lindén, Tom Tetzlaff, Sacha J. van Albada, Sonja Grün, Markus Diesmann, and Gaute T. Einevoll. Hybrid scheme for modeling local field potentials from point-neuron networks. Cerebral Cortex, 26(12):4461–4496, October 2016. URL: http://dx.doi.org/10.1093/cercor/bhw237, doi:10.1093/cercor/bhw237.

• Sandra Diaz-Pier, Mikaël Naveau, Markus Butz-Ostendorf, and Abigail Morrison. Automatic generation of connectivity for large-scale neuronal network models through structural plasticity. Frontiers in Neuroanatomy, May 2016. URL: http://dx.doi.org/10.3389/fnana.2016.00057, doi:10.3389/fnana.2016.00057.

• Nicholas Cain, Ramakrishnan Iyer, Christof Koch, and Stefan Mihalas. The computational properties of a simplified cortical column model. PLOS Computational Biology, 12(9):e1005045, September 2016. URL: http://dx.doi.org/10.1371/journal.pcbi.1005045, doi:10.1371/journal.pcbi.1005045.

• Hannah Bos, Markus Diesmann, and Moritz Helias. Identifying anatomical origins of coexisting oscillations in the cortical microcircuit. PLOS Computational Biology, 12(10):e1005132, October 2016. URL: http://dx.doi.org/10.1371/journal.pcbi.1005132, doi:10.1371/journal.pcbi.1005132.

• Gianvito Urgese, Francesco Barchi, and Enrico Macii. Top-down profiling of application specific many-core neuromorphic platforms. In 2015 IEEE 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, 127–134. IEEE, September 2015. URL: http://dx.doi.org/10.1109/MCSoC.2015.43, doi:10.1109/mcsoc.2015.43.

• Jan Hahne, Moritz Helias, Susanne Kunkel, Jun Igarashi, Matthias Bolten, Andreas Frommer, and Markus Diesmann. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics, September 2015. URL: http://dx.doi.org/10.3389/fninf.2015.00022, doi:10.3389/fninf.2015.00022.

• Nobuhiko Wagatsuma, Tobias C. Potjans, Markus Diesmann, Ko Sakai, and Tomoki Fukai. Spatial and feature-based attention in a layered cortical microcircuit model. PLoS ONE, 8(12):e80788, December 2013. URL: http://dx.doi.org/10.1371/journal.pone.0080788, doi:10.1371/journal.pone.0080788.

All Citations

All publications that cite PD14.

• Naohiro Yamauchi, Yoshimasa Tawatsuji, Yudai Suzuki, Hiroshi Yamakawa, and Kenji Doya. A computational model of canonical cortical microcircuits for dynamic bayesian inference and control as inference. Neuroscience Research, 222:105002, January 2026. URL: http://dx.doi.org/10.1016/j.neures.2025.105002, doi:10.1016/j.neures.2025.105002.

• Zhihe Zhao, Aman S. Aberra, and Alexander Opitz. Simulation of evoked responses to transcranial magnetic stimulation using a multiscale cortical circuit model. Brain Stimulation, 19(1):102983, January 2026. URL: http://dx.doi.org/10.1016/j.brs.2025.11.010, doi:10.1016/j.brs.2025.11.010.

• Michael W Reimann, Sirio Bolaños-Puchet, Jean-Denis Courcol, Daniela Egas Santander, Alexis Arnaudon, Benoît Coste, Fabien Delalondre, Thomas Delemontex, Adrien Devresse, Hugo Dictus, Alexander Dietz, András Ecker, Cyrille Favreau, Gianluca Ficarelli, Mike Gevaert, Joni Herttuainen, James B Isbister, Lida Kanari, Daniel Keller, James King, Pramod Kumbhar, Samuel Lapere, Jãnis Lazovskis, Huanxiang Lu, Nicolas Ninin, Fernando Pereira, Judit Planas, Christoph Pokorny, Juan Luis Riquelme, Armando Romani, Ying Shi, Jason P Smith, Vishal Sood, Mohit Srivastava, Werner Van Geit, Liesbeth Vanherpe, Matthias Wolf, Ran Levi, Kathryn Hess, Felix Schürmann, Eilif B Muller, Henry Markram, and Srikanth Ramaswamy. Modeling and simulation of neocortical micro- and mesocircuitry (part i, anatomy). eLife, January 2026. URL: http://dx.doi.org/10.7554/elife.99688.3, doi:10.7554/elife.99688.3.

• Tibor Rózsa, Rémy Cagnol, and Ján Antolík. Iso-orientation bias of layer 2/3 connections unifies spontaneous, visually and optogenetically driven v1 dynamics. Nature Communications, January 2026. URL: http://dx.doi.org/10.1038/s41467-026-68578-y, doi:10.1038/s41467-026-68578-y.

• Francisco Páscoa dos Santos and Paul F. M. J. Verschure. Excitatory-inhibitory homeostasis and bifurcation control in the wilson-cowan model of cortical dynamics. PLOS Computational Biology, 21(1):e1012723, January 2025. URL: http://dx.doi.org/10.1371/journal.pcbi.1012723, doi:10.1371/journal.pcbi.1012723.

• Nobuhiko Wagatsuma, Yuka Terada, Hiroyuki Okuno, and Natsumi Ageta-Ishihara. Local connections among excitatory neurons underlie characteristics of enriched environment exposure-induced neuronal response modulation in layers 2/3 of the mouse v1. Frontiers in Systems Neuroscience, February 2025. URL: http://dx.doi.org/10.3389/fnsys.2025.1525717, doi:10.3389/fnsys.2025.1525717.

• Ankur Sinha, Padraig Gleeson, Bóris Marin, Salvador Dura-Bernal, Sotirios Panagiotou, Sharon Crook, Matteo Cantarelli, Robert C Cannon, Andrew P Davison, Harsha Gurnani, and Robin Angus Silver. The neuroml ecosystem for standardized multi-scale modeling in neuroscience. eLife, January 2025. URL: http://dx.doi.org/10.7554/elife.95135.3, doi:10.7554/elife.95135.3.

• Anton Arkhipov, Nuno da Costa, Saskia de Vries, Trygve Bakken, Corbett Bennett, Amy Bernard, Jim Berg, Michael Buice, Forrest Collman, Tanya Daigle, Marina Garrett, Nathan Gouwens, Peter A. Groblewski, Julie Harris, Michael Hawrylycz, Rebecca Hodge, Tim Jarsky, Brian Kalmbach, Jerome Lecoq, Brian Lee, Ed Lein, Boaz Levi, Stefan Mihalas, Lydia Ng, Shawn Olsen, Clay Reid, Joshua H. Siegle, Staci Sorensen, Bosiljka Tasic, Carol Thompson, Jonathan T. Ting, Cindy van Velthoven, Shenqin Yao, Zizhen Yao, Christof Koch, and Hongkui Zeng. Integrating multimodal data to understand cortical circuit architecture and function. Nature Neuroscience, 28(4):717–730, March 2025. URL: http://dx.doi.org/10.1038/s41593-025-01904-7, doi:10.1038/s41593-025-01904-7.

• Giulia Moreni, Licheng Zou, Cyriel M. A. Pennartz, and Jorge F. Mejias. Synaptic plasticity facilitates oscillations in a v1 cortical column model with multiple interneuron types. Frontiers in Computational Neuroscience, April 2025. URL: http://dx.doi.org/10.3389/fncom.2025.1568143, doi:10.3389/fncom.2025.1568143.

• José Villamar, Matthias Kelbling, Heather L. More, Michael Denker, Tom Tetzlaff, Johanna Senk, and Stephan Thober. Metadata practices for simulation workflows. Scientific Data, June 2025. URL: http://dx.doi.org/10.1038/s41597-025-05126-1, doi:10.1038/s41597-025-05126-1.

• Alejandro Orozco Valero, Víctor Rodríguez-González, Noemi Montobbio, Miguel A. Casal, Alejandro Tlaie, Francisco Pelayo, Christian Morillas, Jesús Poza, Carlos Gómez, and Pablo Martínez-Cañada. A python toolbox for neural circuit parameter inference. npj Systems Biology and Applications, May 2025. URL: http://dx.doi.org/10.1038/s41540-025-00527-9, doi:10.1038/s41540-025-00527-9.

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• Giulia Moreni, Rares A Dorcioman, Cyriel M. A. Pennartz, and Jorge F. Mejías. Cell-type-specific firing patterns in a v1 cortical column model depend on feedforward and feedback-driven states. PLoS Computational Biology, 21:e1012036–e1012036, 04 2025. URL: https://doi.org/10.1371/journal.pcbi.1012036, doi:10.1371/journal.pcbi.1012036.

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• missing journal in P_scoa_dos_Santos_2024

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• Kyle Aitken, Luke Campagnola, Marina E. Garrett, Shawn R. Olsen, and Stefan Mihalas. Simple synaptic modulations implement diverse novelty computations. Cell Reports, 43(5):114188, May 2024. URL: http://dx.doi.org/10.1016/j.celrep.2024.114188, doi:10.1016/j.celrep.2024.114188.

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• Lysea Haggie, Thor Besier, and Angus McMorland. Circuits in the motor cortex explain oscillatory responses to transcranial magnetic stimulation. Network Neuroscience, 8(1):96–118, 2024. URL: http://dx.doi.org/10.1162/netn_a_00341, doi:10.1162/netn_a_00341.

• Rodrigo Castro, Mariana Bergonzi, Ezequiel Pecker Marcosig, Joaquín Fernández, and Ernesto Kofman. Discrete-event simulation of continuous-time systems: evolution and state of the art of quantized state system methods. SIMULATION, 100(6):613–638, March 2024. URL: http://dx.doi.org/10.1177/00375497241230985, doi:10.1177/00375497241230985.

• Nicolò Meneghetti, Eleonora Vannini, and Alberto Mazzoni. Rodents’ visual gamma as a biomarker of pathological neural conditions. The Journal of Physiology, 602(6):1017–1048, February 2024. URL: http://dx.doi.org/10.1113/jp283858, doi:10.1113/jp283858.

• Chaofei Hong, Mengwen Yuan, Mengxiao Zhang, Xiao Wang, Chengjun Zhang, Jiaxin Wang, Gang Pan, and Huajin Tang. Spaic: a spike-based artificial intelligence computing framework. IEEE Computational Intelligence Magazine, 19(1):51–65, February 2024. URL: http://dx.doi.org/10.1109/mci.2023.3327842, doi:10.1109/mci.2023.3327842.

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• Tomas Barta and Lubomir Kostal. Shared input and recurrency in neural networks for metabolically efficient information transmission. PLOS Computational Biology, 20(2):e1011896, February 2024. URL: http://dx.doi.org/10.1371/journal.pcbi.1011896, doi:10.1371/journal.pcbi.1011896.

• Rowan Hodson, Marishka Mehta, and Ryan Smith. The empirical status of predictive coding and active inference. Neuroscience & Biobehavioral Reviews, 157:105473, February 2024. URL: http://dx.doi.org/10.1016/j.neubiorev.2023.105473, doi:10.1016/j.neubiorev.2023.105473.

• Srikanth Ramaswamy. Data-driven multiscale computational models of cortical and subcortical regions. Current Opinion in Neurobiology, 85:102842, April 2024. URL: http://dx.doi.org/10.1016/j.conb.2024.102842, doi:10.1016/j.conb.2024.102842.

• Hans Ekkehard Plesser. Commentary: accelerating spiking neural network simulations with pymonnto and pymonntorch. Frontiers in Neuroinformatics, October 2024. URL: http://dx.doi.org/10.3389/fninf.2024.1446620, doi:10.3389/fninf.2024.1446620.

• Yin-Jui Chang, Yuan-I Chen, Hannah M. Stealey, Yi Zhao, Hung-Yun Lu, Enrique Contreras-Hernandez, Megan N. Baker, Edward Castillo, Hsin-Chih Yeh, and Samantha R. Santacruz. Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations. PLOS ONE, 19(12):e0314268, December 2024. URL: http://dx.doi.org/10.1371/journal.pone.0314268, doi:10.1371/journal.pone.0314268.

• Johanna Senk, Espen Hagen, Sacha J. van Albada, and Markus Diesmann. Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space. Cerebral Cortex, 09 2024. URL: https://doi.org/10.1093/cercor/bhae405, doi:10.1093/cercor/bhae405.

• Jun Igarashi. Future projections for mammalian whole-brain simulations based on technological trends in related fields. Neuroscience Research, 11 2024. URL: https://doi.org/10.1016/j.neures.2024.11.005, doi:10.1016/j.neures.2024.11.005.

• Jan-Eirik W. Skaar, Nicolai Haug, Alexander J. Stasik, Gaute T. Einevoll, and Kristin Tøndel. Metamodelling of a two-population spiking neural network. PLOS Computational Biology, 19(11):e1011625, November 2023. URL: http://dx.doi.org/10.1371/journal.pcbi.1011625, doi:10.1371/journal.pcbi.1011625.

• Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li, and Si Wu. Brainpy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife, December 2023. URL: http://dx.doi.org/10.7554/elife.86365, doi:10.7554/elife.86365.

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• Mariana Bergonzi, Joaquín Fernández, Rodrigo Castro, Alexandre Muzy, and Ernesto Kofman. Quantization-based simulation of spiking neurons: theoretical properties and performance analysis. Journal of Simulation, 18(5):789–812, November 2023. URL: http://dx.doi.org/10.1080/17477778.2023.2284143, doi:10.1080/17477778.2023.2284143.

• Nestor Timonidis, Mario Rubio-Teves, Carmen Alonso-Martínez, Rembrandt Bakker, María García-Amado, Paul Tiesinga, and Francisco Clascá. Analyzing thalamocortical tract-tracing experiments in a common reference space. Neuroinformatics, 22(1):23–43, October 2023. URL: http://dx.doi.org/10.1007/s12021-023-09644-4, doi:10.1007/s12021-023-09644-4.

• Angeliki Lorents, Marie-Elisabeth Colin, Ingvild Elise Bjerke, Simon Nougaret, Luca Montelisciani, Marissa Diaz, Paul Verschure, and Julien Vezoli. Human brain project partnering projects meeting: status quo and outlook. eneuro, 10(9):ENEURO.0091–23.2023, September 2023. URL: http://dx.doi.org/10.1523/eneuro.0091-23.2023, doi:10.1523/eneuro.0091-23.2023.

• Farshad Shirani and Hannah Choi. On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks. Journal of Computational Neuroscience, 52(1):73–107, October 2023. URL: http://dx.doi.org/10.1007/s10827-023-00863-x, doi:10.1007/s10827-023-00863-x.

• Evan Cudone, Amelia M. Lower, and Robert A. McDougal. Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories. PLOS Computational Biology, 19(10):e1011548, October 2023. URL: http://dx.doi.org/10.1371/journal.pcbi.1011548, doi:10.1371/journal.pcbi.1011548.

• Valentin Schmutz, Eva Löcherbach, and Tilo Schwalger. On a finite-size neuronal population equation. SIAM Journal on Applied Dynamical Systems, 22(2):996–1029, June 2023. URL: http://dx.doi.org/10.1137/21m1445041, doi:10.1137/21m1445041.

• Salvador Dura-Bernal, Samuel A. Neymotin, Benjamin A. Suter, Joshua Dacre, Joao V.S. Moreira, Eugenio Urdapilleta, Julia Schiemann, Ian Duguid, Gordon M.G. Shepherd, and William W. Lytton. Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics. Cell Reports, 42(6):112574, June 2023. URL: http://dx.doi.org/10.1016/j.celrep.2023.112574, doi:10.1016/j.celrep.2023.112574.

• Darrell Haufler, Shinya Ito, Christof Koch, and Anton Arkhipov. Simulations of cortical networks using spatially extended conductance‐based neuronal models. The Journal of Physiology, 601(15):3123–3139, January 2023. URL: http://dx.doi.org/10.1113/jp284030, doi:10.1113/jp284030.

• Kevin Kauth, Tim Stadtmann, Vida Sobhani, and Tobias Gemmeke. Neuroaix-framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time. Frontiers in Computational Neuroscience, April 2023. URL: http://dx.doi.org/10.3389/fncom.2023.1144143, doi:10.3389/fncom.2023.1144143.

• Sergey V. Stasenko and Victor B. Kazantsev. Information encoding in bursting spiking neural network modulated by astrocytes. Entropy, 25(5):745, May 2023. URL: http://dx.doi.org/10.3390/e25050745, doi:10.3390/e25050745.

• Qianming Ding, Yong Wu, Tianyu Li, Dong Yu, and Ya Jia. Metabolic energy consumption and information transmission of a two-compartment neuron model and its cortical network. Chaos, Solitons & Fractals, 171:113464, June 2023. URL: http://dx.doi.org/10.1016/j.chaos.2023.113464, doi:10.1016/j.chaos.2023.113464.

• Lysea Haggie, Laura Schmid, Oliver Röhrle, Thor Besier, Angus McMorland, and Harnoor Saini. Linking cortex and contraction—integrating models along the corticomuscular pathway. Frontiers in Physiology, May 2023. URL: http://dx.doi.org/10.3389/fphys.2023.1095260, doi:10.3389/fphys.2023.1095260.

• Wei Zhang, Muqi Yin, Mingfeng Jiang, and Qi Dai. Partitioned estimation methodology of biological neuronal networks with topology-based module detection. Computers in Biology and Medicine, 154:106552, March 2023. URL: http://dx.doi.org/10.1016/j.compbiomed.2023.106552, doi:10.1016/j.compbiomed.2023.106552.

• Richard Gast, Sara A. Solla, and Ann Kennedy. Macroscopic dynamics of neural networks with heterogeneous spiking thresholds. Physical Review E, February 2023. URL: http://dx.doi.org/10.1103/physreve.107.024306, doi:10.1103/physreve.107.024306.

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• Robert E. Kass, Heejong Bong, Motolani Olarinre, Qi Xin, and Konrad N. Urban. Identification of interacting neural populations: methods and statistical considerations. Journal of Neurophysiology, 130(3):475–496, September 2023. URL: http://dx.doi.org/10.1152/jn.00131.2023, doi:10.1152/jn.00131.2023.

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• Felix Schürmann, Jean-Denis Courcol, and Srikanth Ramaswamy. Computational Concepts for Reconstructing and Simulating Brain Tissue, pages 237–259. Springer International Publishing, 2022. URL: http://dx.doi.org/10.1007/978-3-030-89439-9_10, doi:10.1007/978-3-030-89439-9_10.

• J. Pronold, J. Jordan, B.J.N. Wylie, I. Kitayama, M. Diesmann, and S. Kunkel. Routing brain traffic through the von neumann bottleneck: efficient cache usage in spiking neural network simulation code on general purpose computers. Parallel Computing, 113:102952, October 2022. URL: http://dx.doi.org/10.1016/j.parco.2022.102952, doi:10.1016/j.parco.2022.102952.

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• Ludovico Minati, Jim Bartels, Chao Li, Mattia Frasca, and Hiroyuki Ito. Synchronization phenomena in dual-transistor spiking oscillators realized experimentally towards physical reservoirs. Chaos, Solitons & Fractals, 162:112415, September 2022. URL: http://dx.doi.org/10.1016/j.chaos.2022.112415, doi:10.1016/j.chaos.2022.112415.

• Tom Bugnon, William G. P. Mayner, Chiara Cirelli, and Giulio Tononi. Sleep and wake in a model of the thalamocortical system with martinotti cells. European Journal of Neuroscience, 59(4):703–736, November 2022. URL: http://dx.doi.org/10.1111/ejn.15836, doi:10.1111/ejn.15836.

• Alessio Quaresima, Hartmut Fitz, Renato Duarte, Dick van den Broek, Peter Hagoort, and Karl Magnus Petersson. The tripod neuron: a minimal structural reduction of the dendritic tree. The Journal of Physiology, 601(15):3265–3295, November 2022. URL: http://dx.doi.org/10.1113/jp283399, doi:10.1113/jp283399.

• Espen Hagen, Steinn H. Magnusson, Torbjørn V. Ness, Geir Halnes, Pooja N. Babu, Charl Linssen, Abigail Morrison, and Gaute T. Einevoll. Brain signal predictions from multi-scale networks using a linearized framework. PLOS Computational Biology, 18(8):e1010353, August 2022. URL: http://dx.doi.org/10.1371/journal.pcbi.1010353, doi:10.1371/journal.pcbi.1010353.

• Jung Hoon Lee, Joji Tsunada, Sujith Vijayan, and Yale E. Cohen. Cortical circuit-based lossless neural integrator for perceptual decision-making: a computational modeling study. Frontiers in Computational Neuroscience, November 2022. URL: http://dx.doi.org/10.3389/fncom.2022.979830, doi:10.3389/fncom.2022.979830.

• Hugh Osborne and Marc de Kamps. A numerical population density technique for n-dimensional neuron models. Frontiers in Neuroinformatics, July 2022. URL: http://dx.doi.org/10.3389/fninf.2022.883796, doi:10.3389/fninf.2022.883796.

• Fernando S. Borges, Joao V. S. Moreira, Lavinia M. Takarabe, William W. Lytton, and Salvador Dura-Bernal. Large-scale biophysically detailed model of somatosensory thalamocortical circuits in netpyne. Frontiers in Neuroinformatics, September 2022. URL: http://dx.doi.org/10.3389/fninf.2022.884245, doi:10.3389/fninf.2022.884245.

• Robert Kleijnen, Markus Robens, Michael Schiek, and Stefan van Waasen. Verification of a neuromorphic computing network simulator using experimental traffic data. Frontiers in Neuroscience, August 2022. URL: http://dx.doi.org/10.3389/fnins.2022.958343, doi:10.3389/fnins.2022.958343.

• Shailesh Appukuttan and Andrew P. Davison. Reproducing and quantitatively validating a biologically-constrained point-neuron model of ca1 pyramidal cells. Frontiers in Integrative Neuroscience, November 2022. URL: http://dx.doi.org/10.3389/fnint.2022.1041423, doi:10.3389/fnint.2022.1041423.

• Tobias Schulte to Brinke, Renato Duarte, and Abigail Morrison. Characteristic columnar connectivity caters to cortical computation: replication, simulation, and evaluation of a microcircuit model. Frontiers in Integrative Neuroscience, October 2022. URL: http://dx.doi.org/10.3389/fnint.2022.923468, doi:10.3389/fnint.2022.923468.

• Liwei Yang, Huaipeng Zhang, Tao Luo, Chuping Qu, Myat Thu Linn Aung, Yingnan Cui, Jun Zhou, Ming Ming Wong, Junran Pu, Anh Tuan Do, Rick Siow Mong Goh, and Weng Fai Wong. Coreset: hierarchical neuromorphic computing supporting large-scale neural networks with improved resource efficiency. Neurocomputing, 474:128–140, February 2022. URL: http://dx.doi.org/10.1016/j.neucom.2021.12.021, doi:10.1016/j.neucom.2021.12.021.

• Patrick Herbers, Iago Calvo, Sandra Diaz-Pier, Oscar D. Robles, Susana Mata, Pablo Toharia, Luis Pastor, Alexander Peyser, Abigail Morrison, and Wouter Klijn. Congen—a simulator-agnostic visual language for definition and generation of connectivity in large and multiscale neural networks. Frontiers in Neuroinformatics, January 2022. URL: http://dx.doi.org/10.3389/fninf.2021.766697, doi:10.3389/fninf.2021.766697.

• Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, and Moritz Helias. Nnmt: mean-field based analysis tools for neuronal network models. Frontiers in Neuroinformatics, May 2022. URL: http://dx.doi.org/10.3389/fninf.2022.835657, doi:10.3389/fninf.2022.835657.

• Gianmarco Tiddia, Bruno Golosio, Jasper Albers, Johanna Senk, Francesco Simula, Jari Pronold, Viviana Fanti, Elena Pastorelli, Pier Stanislao Paolucci, and Sacha J. van Albada. Fast simulation of a multi-area spiking network model of macaque cortex on an mpi-gpu cluster. Frontiers in Neuroinformatics, July 2022. URL: http://dx.doi.org/10.3389/fninf.2022.883333, doi:10.3389/fninf.2022.883333.

• Guido Trensch and Abigail Morrison. A system-on-chip based hybrid neuromorphic compute node architecture for reproducible hyper-real-time simulations of spiking neural networks. Frontiers in Neuroinformatics, June 2022. URL: http://dx.doi.org/10.3389/fninf.2022.884033, doi:10.3389/fninf.2022.884033.

• Philipp Haueis. Descriptive multiscale modeling in data-driven neuroscience. Synthese, April 2022. URL: http://dx.doi.org/10.1007/s11229-022-03551-y, doi:10.1007/s11229-022-03551-y.

• Chao Huang, Fleur Zeldenrust, and Tansu Celikel. Cortical representation of touch in silico. Neuroinformatics, 20(4):1013–1039, April 2022. URL: http://dx.doi.org/10.1007/s12021-022-09576-5, doi:10.1007/s12021-022-09576-5.

• Anno C Kurth, Johanna Senk, Dennis Terhorst, Justin Finnerty, and Markus Diesmann. Sub-realtime simulation of a neuronal network of natural density. Neuromorphic Computing and Engineering, 2(2):021001, March 2022. URL: http://dx.doi.org/10.1088/2634-4386/ac55fc, doi:10.1088/2634-4386/ac55fc.

• Katrin Amunts, Javier DeFelipe, Cyriel Pennartz, Alain Destexhe, Michele Migliore, Philippe Ryvlin, Steve Furber, Alois Knoll, Lise Bitsch, Jan G. Bjaalie, Yannis Ioannidis, Thomas Lippert, Maria V. Sanchez-Vives, Rainer Goebel, and Viktor Jirsa. Linking brain structure, activity, and cognitive function through computation. eneuro, 9(2):ENEURO.0316–21.2022, February 2022. URL: http://dx.doi.org/10.1523/eneuro.0316-21.2022, doi:10.1523/eneuro.0316-21.2022.

• H. Y. Li, G. M. Cheng, and Emily S. C. Ching. Heterogeneous responses to changes in inhibitory synaptic strength in networks of spiking neurons. Frontiers in Cellular Neuroscience, February 2022. URL: http://dx.doi.org/10.3389/fncel.2022.785207, doi:10.3389/fncel.2022.785207.

• Nikola Jajcay, Caglar Cakan, and Klaus Obermayer. Cross-frequency slow oscillation–spindle coupling in a biophysically realistic thalamocortical neural mass model. Frontiers in Computational Neuroscience, May 2022. URL: http://dx.doi.org/10.3389/fncom.2022.769860, doi:10.3389/fncom.2022.769860.

• Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, and Susanne Kunkel. Routing brain traffic through the von neumann bottleneck: parallel sorting and refactoring. Frontiers in Neuroinformatics, March 2022. URL: http://dx.doi.org/10.3389/fninf.2021.785068, doi:10.3389/fninf.2021.785068.

• Oliver Maith, Helge Ülo Dinkelbach, Javier Baladron, Julien Vitay, and Fred H. Hamker. Bold monitoring in the neural simulator annarchy. Frontiers in Neuroinformatics, March 2022. URL: http://dx.doi.org/10.3389/fninf.2022.790966, doi:10.3389/fninf.2022.790966.

• Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, and Johanna Senk. A modular workflow for performance benchmarking of neuronal network simulations. Frontiers in Neuroinformatics, May 2022. URL: http://dx.doi.org/10.3389/fninf.2022.837549, doi:10.3389/fninf.2022.837549.

• Arne Heittmann, Georgia Psychou, Guido Trensch, Charles E. Cox, Winfried W. Wilcke, Markus Diesmann, and Tobias G. Noll. Simulating the cortical microcircuit significantly faster than real time on the ibm inc-3000 neural supercomputer. Frontiers in Neuroscience, January 2022. URL: http://dx.doi.org/10.3389/fnins.2021.728460, doi:10.3389/fnins.2021.728460.

• Nishant Mysore, Gopabandhu Hota, Stephen R. Deiss, Bruno U. Pedroni, and Gert Cauwenberghs. Hierarchical network connectivity and partitioning for reconfigurable large-scale neuromorphic systems. Frontiers in Neuroscience, January 2022. URL: http://dx.doi.org/10.3389/fnins.2021.797654, doi:10.3389/fnins.2021.797654.

• Luca Peres and Oliver Rhodes. Parallelization of neural processing on neuromorphic hardware. Frontiers in Neuroscience, May 2022. URL: http://dx.doi.org/10.3389/fnins.2022.867027, doi:10.3389/fnins.2022.867027.

• Robert Kleijnen, Markus Robens, Michael Schiek, and Stefan van Waasen. A network simulator for the estimation of bandwidth load and latency created by heterogeneous spiking neural networks on neuromorphic computing communication networks. Journal of Low Power Electronics and Applications, 12(2):23, April 2022. URL: http://dx.doi.org/10.3390/jlpea12020023, doi:10.3390/jlpea12020023.

• David Dahmen, Moritz Layer, Lukas Deutz, Paulina Anna Dąbrowska, Nicole Voges, Michael von Papen, Thomas Brochier, Alexa Riehle, Markus Diesmann, Sonja Grün, and Moritz Helias. Global organization of neuronal activity only requires unstructured local connectivity. eLife, January 2022. URL: http://dx.doi.org/10.7554/elife.68422, doi:10.7554/elife.68422.

• Alessandro Panarese, Matteo Vissani, Nicolò Meneghetti, Eleonora Vannini, Marina Cracchiolo, Silvestro Micera, Matteo Caleo, Alberto Mazzoni, and Laura Restani. Disruption of layer-specific visual processing in a model of focal neocortical epilepsy. Cerebral Cortex, 33(7):4173–4187, September 2022. URL: http://dx.doi.org/10.1093/cercor/bhac335, doi:10.1093/cercor/bhac335.

• Johanna Senk, Birgit Kriener, Mikael Djurfeldt, Nicole Voges, Han-Jia Jiang, Lisa Schüttler, Gabriele Gramelsberger, Markus Diesmann, Hans Ekkehard Pleßer, and Sacha J. van Albada. Connectivity concepts in neuronal network modeling. PLoS Computational Biology, 18:e1010086–e1010086, 09 2022. URL: https://doi.org/10.1371/journal.pcbi.1010086, doi:10.1371/journal.pcbi.1010086.

• Hugh Osborne, Lukas Deutz, and Marc de Kamps. Multidimensional Dynamical Systems with Noise: Population Density Techniques for Neuroscience, pages 159–178. Springer International Publishing, October 2021. URL: http://dx.doi.org/10.1007/978-3-030-89439-9_7, doi:10.1007/978-3-030-89439-9_7.

• R. Kleijnen, M. Robens, M. Schiek, and S. van Waasen. A network simulator for the estimation of bandwidth load and latency created by heterogeneous spiking neural networks on neuromorphic computing communication networks. In 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 320–327. IEEE, December 2021. URL: http://dx.doi.org/10.1109/mcsoc51149.2021.00054, doi:10.1109/mcsoc51149.2021.00054.

• Jun Igarashi. The future of mammalian whole-brain simulations estimated from technological trends in supercomputers and brain measurements. The Brain & Neural Networks, 28(4):172–182, December 2021. URL: http://dx.doi.org/10.3902/jnns.28.172, doi:10.3902/jnns.28.172.

• missing author in 2021

• Tilo Schwalger. Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach. Biological Cybernetics, 115(5):539–562, October 2021. URL: http://dx.doi.org/10.1007/s00422-021-00899-1, doi:10.1007/s00422-021-00899-1.

• G. Giacopelli, D. Tegolo, and M. Migliore. The role of network connectivity on epileptiform activity. Scientific Reports, October 2021. URL: http://dx.doi.org/10.1038/s41598-021-00283-w, doi:10.1038/s41598-021-00283-w.

• Leonardo Dalla Porta, Daniel M. Castro, Mauro Copelli, Pedro V. Carelli, and Fernanda S. Matias. Feedforward and feedback influences through distinct frequency bands between two spiking-neuron networks. Physical Review E, November 2021. URL: http://dx.doi.org/10.1103/physreve.104.054404, doi:10.1103/physreve.104.054404.

• Alexander van Meegen and Sacha J. van Albada. Microscopic theory of intrinsic timescales in spiking neural networks. Physical Review Research, October 2021. URL: http://dx.doi.org/10.1103/physrevresearch.3.043077, doi:10.1103/physrevresearch.3.043077.

• René Larisch, Lorenz Gönner, Michael Teichmann, and Fred H. Hamker. Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLOS Computational Biology, 17(11):e1009566, November 2021. URL: http://dx.doi.org/10.1371/journal.pcbi.1009566, doi:10.1371/journal.pcbi.1009566.

• Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, and Johanna Senk. Dynamical characteristics of recurrent neuronal networks are robust against low synaptic weight resolution. Frontiers in Neuroscience, December 2021. URL: http://dx.doi.org/10.3389/fnins.2021.757790, doi:10.3389/fnins.2021.757790.

• Alexander Telnykh, Irina Nuidel, Olga Shemagina, and Vladimir Yakhno. A biomorphic model of cortical column for content—based image retrieval. Entropy, 23(11):1458, November 2021. URL: http://dx.doi.org/10.3390/e23111458, doi:10.3390/e23111458.

• Qian Zhang, Yi Zeng, Tielin Zhang, and Taoyi Yang. Comparison between human and rodent neurons for persistent activity performance: a biologically plausible computational investigation. Frontiers in Systems Neuroscience, September 2021. URL: http://dx.doi.org/10.3389/fnsys.2021.628839, doi:10.3389/fnsys.2021.628839.

• Michael Teichmann, René Larisch, and Fred H. Hamker. Performance of biologically grounded models of the early visual system on standard object recognition tasks. Neural Networks, 144:210–228, December 2021. URL: http://dx.doi.org/10.1016/j.neunet.2021.08.009, doi:10.1016/j.neunet.2021.08.009.

• Alejandro Santos-Mayo, Stephan Moratti, Javier de Echegaray, and Gianluca Susi. A model of the early visual system based on parallel spike-sequence detection, showing orientation selectivity. Biology, 10(8):801, August 2021. URL: http://dx.doi.org/10.3390/biology10080801, doi:10.3390/biology10080801.

• Hugh Osborne, Yi Ming Lai, Mikkel Elle Lepperød, David Sichau, Lukas Deutz, and Marc de Kamps. Miind : a model-agnostic simulator of neural populations. Frontiers in Neuroinformatics, July 2021. URL: http://dx.doi.org/10.3389/fninf.2021.614881, doi:10.3389/fninf.2021.614881.

• Friedemann Pulvermüller, Rosario Tomasello, Malte R. Henningsen-Schomers, and Thomas Wennekers. Biological constraints on neural network models of cognitive function. Nature Reviews Neuroscience, 22(8):488–502, June 2021. URL: http://dx.doi.org/10.1038/s41583-021-00473-5, doi:10.1038/s41583-021-00473-5.

• Renan Oliveira Shimoura, Rodrigo F. O. Pena, Vinicius Lima, Nilton L. Kamiji, Mauricio Girardi-Schappo, and Antonio C. Roque. Building a model of the brain: from detailed connectivity maps to network organization. The European Physical Journal Special Topics, 230(14–15):2887–2909, June 2021. URL: http://dx.doi.org/10.1140/epjs/s11734-021-00152-7, doi:10.1140/epjs/s11734-021-00152-7.

• Cecilia Romaro, Fernando Araujo Najman, William W. Lytton, Antonio C. Roque, and Salvador Dura-Bernal. Netpyne implementation and scaling of the potjans-diesmann cortical microcircuit model. Neural Computation, 33(7):1993–2032, June 2021. URL: http://dx.doi.org/10.1162/neco_a_01400, doi:10.1162/neco_a_01400.

• Ramin Hasani, Giorgio Ferrari, Hideaki Yamamoto, Takashi Tanii, and Enrico Prati. Role of noise in spontaneous activity of networks of neurons on patterned silicon emulated by noise–activated cmos neural nanoelectronic circuits. Nano Express, 2(2):020025, June 2021. URL: http://dx.doi.org/10.1088/2632-959x/abf2ae, doi:10.1088/2632-959x/abf2ae.

• Paulina Anna Dąbrowska, Nicole Voges, Michael von Papen, Junji Ito, David Dahmen, Alexa Riehle, Thomas Brochier, and Sonja Grün. On the complexity of resting state spiking activity in monkey motor cortex. Cerebral Cortex Communications, January 2021. URL: http://dx.doi.org/10.1093/texcom/tgab033, doi:10.1093/texcom/tgab033.

• James C. Knight, Anton Komissarov, and Thomas Nowotny. Pygenn: a python library for gpu-enhanced neural networks. Frontiers in Neuroinformatics, April 2021. URL: http://dx.doi.org/10.3389/fninf.2021.659005, doi:10.3389/fninf.2021.659005.

• Paulo R. Protachevicz, Matheus Hansen, Kelly C. Iarosz, Iberê L. Caldas, Antonio M. Batista, and Jürgen Kurths. Emergence of neuronal synchronisation in coupled areas. Frontiers in Computational Neuroscience, April 2021. URL: http://dx.doi.org/10.3389/fncom.2021.663408, doi:10.3389/fncom.2021.663408.

• Pablo Martínez-Cañada, Torbjørn V. Ness, Gaute T. Einevoll, Tommaso Fellin, and Stefano Panzeri. Computation of the electroencephalogram (eeg) from network models of point neurons. PLOS Computational Biology, 17(4):e1008893, April 2021. URL: http://dx.doi.org/10.1371/journal.pcbi.1008893, doi:10.1371/journal.pcbi.1008893.

• Ian Cone and Harel Z Shouval. Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network. eLife, March 2021. URL: http://dx.doi.org/10.7554/elife.63751, doi:10.7554/elife.63751.

• Philipp Haueis. Multiscale modeling of cortical gradients: the role of mesoscale circuits for linking macro- and microscale gradients of cortical organization and hierarchical information processing. NeuroImage, 232:117846, May 2021. URL: http://dx.doi.org/10.1016/j.neuroimage.2021.117846, doi:10.1016/j.neuroimage.2021.117846.

• Giuseppe Giacopelli, Domenico Tegolo, Emiliano Spera, and Michele Migliore. On the structural connectivity of large-scale models of brain networks at cellular level. Scientific Reports, February 2021. URL: http://dx.doi.org/10.1038/s41598-021-83759-z, doi:10.1038/s41598-021-83759-z.

• Bruno Golosio, Gianmarco Tiddia, Chiara De Luca, Elena Pastorelli, Francesco Simula, and Pier Stanislao Paolucci. Fast simulations of highly-connected spiking cortical models using gpus. Frontiers in Computational Neuroscience, February 2021. URL: http://dx.doi.org/10.3389/fncom.2021.627620, doi:10.3389/fncom.2021.627620.

• James C. Knight and Thomas Nowotny. Larger gpu-accelerated brain simulations with procedural connectivity. Nature Computational Science, 1(2):136–142, February 2021. URL: http://dx.doi.org/10.1038/s43588-020-00022-7, doi:10.1038/s43588-020-00022-7.

• Margarita Zachariou, Mark J. Roberts, Eric Lowet, Peter De Weerd, and Avgis Hadjipapas. Empirically constrained network models for contrast-dependent modulation of gamma rhythm in v1. NeuroImage, 229:117748, April 2021. URL: http://dx.doi.org/10.1016/j.neuroimage.2021.117748, doi:10.1016/j.neuroimage.2021.117748.

• Tawan T. A. Carvalho, Antonio J. Fontenele, Mauricio Girardi-Schappo, Thaís Feliciano, Leandro A. A. Aguiar, Thais P. L. Silva, Nivaldo A. P. de Vasconcelos, Pedro V. Carelli, and Mauro Copelli. Subsampled directed-percolation models explain scaling relations experimentally observed in the brain. Frontiers in Neural Circuits, January 2021. URL: http://dx.doi.org/10.3389/fncir.2020.576727, doi:10.3389/fncir.2020.576727.

• Solveig Næss, Geir Halnes, Espen Hagen, Donald J. Hagler, Anders M. Dale, Gaute T. Einevoll, and Torbjørn V. Ness. Biophysically detailed forward modeling of the neural origin of eeg and meg signals. NeuroImage, 225:117467, January 2021. URL: http://dx.doi.org/10.1016/j.neuroimage.2020.117467, doi:10.1016/j.neuroimage.2020.117467.

• Michael E. Hasselmo, Andrew S. Alexander, Alec Hoyland, Jennifer C. Robinson, Marianne J. Bezaire, G. William Chapman, Ausra Saudargiene, Lucas C. Carstensen, and Holger Dannenberg. The unexplored territory of neural models: potential guides for exploring the function of metabotropic neuromodulation. Neuroscience, 456:143–158, February 2021. URL: http://dx.doi.org/10.1016/j.neuroscience.2020.03.048, doi:10.1016/j.neuroscience.2020.03.048.

• Jung Hoon Lee. Biologically plausible mechanisms underlying motor response correction during reward-based decision-making. Neurocomputing, 412:416–425, October 2020. URL: http://dx.doi.org/10.1016/j.neucom.2020.06.103, doi:10.1016/j.neucom.2020.06.103.

• Dongcheng Zhao, Yi Zeng, Tielin Zhang, Mengting Shi, and Feifei Zhao. Glsnn: a multi-layer spiking neural network based on global feedback alignment and local stdp plasticity. Frontiers in Computational Neuroscience, November 2020. URL: http://dx.doi.org/10.3389/fncom.2020.576841, doi:10.3389/fncom.2020.576841.

• A D Bird, L H Deters, and H Cuntz. Excess neuronal branching allows for local innervation of specific dendritic compartments in mature cortex. Cerebral Cortex, 31(2):1008–1031, October 2020. URL: http://dx.doi.org/10.1093/cercor/bhaa271, doi:10.1093/cercor/bhaa271.

• Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, David S Greenberg, and Jakob H Macke. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, September 2020. URL: http://dx.doi.org/10.7554/elife.56261, doi:10.7554/elife.56261.

• Justen Geddes, Gaute T. Einevoll, Evrim Acar, and Alexander J. Stasik. Multi-linear population analysis (mlpa) of lfp data using tensor decompositions. Frontiers in Applied Mathematics and Statistics, September 2020. URL: http://dx.doi.org/10.3389/fams.2020.00041, doi:10.3389/fams.2020.00041.

• Bastian Pietras, Noé Gallice, and Tilo Schwalger. Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons. Physical Review E, August 2020. URL: http://dx.doi.org/10.1103/physreve.102.022407, doi:10.1103/physreve.102.022407.

• Alexandre René, André Longtin, and Jakob H. Macke. Inference of a mesoscopic population model from population spike trains. Neural Computation, 32(8):1448–1498, August 2020. URL: http://dx.doi.org/10.1162/neco_a_01292, doi:10.1162/neco_a_01292.

• Yuxiu Shao, Jiwei Zhang, and Louis Tao. Dimensional reduction of emergent spatiotemporal cortical dynamics via a maximum entropy moment closure. PLOS Computational Biology, 16(6):e1007265, June 2020. URL: http://dx.doi.org/10.1371/journal.pcbi.1007265, doi:10.1371/journal.pcbi.1007265.

• Johanna Senk, Karolína Korvasová, Jannis Schuecker, Espen Hagen, Tom Tetzlaff, Markus Diesmann, and Moritz Helias. Conditions for wave trains in spiking neural networks. Physical Review Research, May 2020. URL: http://dx.doi.org/10.1103/physrevresearch.2.023174, doi:10.1103/physrevresearch.2.023174.

• Valentin Schmutz, Wulfram Gerstner, and Tilo Schwalger. Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity. The Journal of Mathematical Neuroscience, April 2020. URL: http://dx.doi.org/10.1186/s13408-020-00082-z, doi:10.1186/s13408-020-00082-z.

• Francesco Cremonesi, Georg Hager, Gerhard Wellein, and Felix Schürmann. Analytic performance modeling and analysis of detailed neuron simulations. The International Journal of High Performance Computing Applications, 34(4):428–449, April 2020. URL: http://dx.doi.org/10.1177/1094342020912528, doi:10.1177/1094342020912528.

• Rodrigo F. O. Pena, Vinicius Lima, Renan O. Shimoura, João Paulo Novato, and Antonio C. Roque. Optimal interplay between synaptic strengths and network structure enhances activity fluctuations and information propagation in hierarchical modular networks. Brain Sciences, 10(4):228, April 2020. URL: http://dx.doi.org/10.3390/brainsci10040228, doi:10.3390/brainsci10040228.

• Ashish Raj, Chang Cai, Xihe Xie, Eva Palacios, Julia Owen, Pratik Mukherjee, and Srikantan Nagarajan. Spectral graph theory of brain oscillations. Human Brain Mapping, 41(11):2980–2998, March 2020. URL: http://dx.doi.org/10.1002/hbm.24991, doi:10.1002/hbm.24991.

• Jan-Eirik W. Skaar, Alexander J. Stasik, Espen Hagen, Torbjørn V. Ness, and Gaute T. Einevoll. Estimation of neural network model parameters from local field potentials (lfps). PLOS Computational Biology, 16(3):e1007725, March 2020. URL: http://dx.doi.org/10.1371/journal.pcbi.1007725, doi:10.1371/journal.pcbi.1007725.

• Francesco Cremonesi and Felix Schürmann. Understanding computational costs of cellular-level brain tissue simulations through analytical performance models. Neuroinformatics, 18(3):407–428, February 2020. URL: http://dx.doi.org/10.1007/s12021-019-09451-w, doi:10.1007/s12021-019-09451-w.

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• Robert Turner. Myelin and modeling: bootstrapping cortical microcircuits. Frontiers in Neural Circuits, May 2019. URL: http://dx.doi.org/10.3389/fncir.2019.00034, doi:10.3389/fncir.2019.00034.

• Gaute T. Einevoll, Alain Destexhe, Markus Diesmann, Sonja Grün, Viktor Jirsa, Marc de Kamps, Michele Migliore, Torbjørn V. Ness, Hans E. Plesser, and Felix Schürmann. The scientific case for brain simulations. Neuron, 102(4):735–744, May 2019. URL: http://dx.doi.org/10.1016/j.neuron.2019.03.027, doi:10.1016/j.neuron.2019.03.027.

• Salvador Dura-Bernal, Benjamin A Suter, Padraig Gleeson, Matteo Cantarelli, Adrian Quintana, Facundo Rodriguez, David J Kedziora, George L Chadderdon, Cliff C Kerr, Samuel A Neymotin, Robert A McDougal, Michael Hines, Gordon MG Shepherd, and William W Lytton. Netpyne, a tool for data-driven multiscale modeling of brain circuits. eLife, April 2019. URL: http://dx.doi.org/10.7554/elife.44494, doi:10.7554/elife.44494.

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• Yifan Gu, Yang Qi, and Pulin Gong. Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits. PLOS Computational Biology, 15(4):e1006902, April 2019. URL: http://dx.doi.org/10.1371/journal.pcbi.1006902, doi:10.1371/journal.pcbi.1006902.

• Carlos Fernandez-Musoles, Daniel Coca, and Paul Richmond. Communication sparsity in distributed spiking neural network simulations to improve scalability. Frontiers in Neuroinformatics, April 2019. URL: http://dx.doi.org/10.3389/fninf.2019.00019, doi:10.3389/fninf.2019.00019.

• Andrew G. D. Rowley, Christian Brenninkmeijer, Simon Davidson, Donal Fellows, Andrew Gait, David R. Lester, Luis A. Plana, Oliver Rhodes, Alan B. Stokes, and Steve B. Furber. Spinntools: the execution engine for the spinnaker platform. Frontiers in Neuroscience, March 2019. URL: http://dx.doi.org/10.3389/fnins.2019.00231, doi:10.3389/fnins.2019.00231.

• Christopher Thornton, Frances Hutchings, and Marcus Kaiser. The virtual electrode recording tool for extracellular potentials (vertex) version 2.0: modelling in vitro electrical stimulation of brain tissue. Wellcome Open Research, 4:20, February 2019. URL: http://dx.doi.org/10.12688/wellcomeopenres.15058.1, doi:10.12688/wellcomeopenres.15058.1.

• Andrew T. Reid, Drew B. Headley, Ravi D. Mill, Ruben Sanchez-Romero, Lucina Q. Uddin, Daniele Marinazzo, Daniel J. Lurie, Pedro A. Valdés-Sosa, Stephen José Hanson, Bharat B. Biswal, Vince Calhoun, Russell A. Poldrack, and Michael W. Cole. Advancing functional connectivity research from association to causation. Nature Neuroscience, 22(11):1751–1760, October 2019. URL: http://dx.doi.org/10.1038/s41593-019-0510-4, doi:10.1038/s41593-019-0510-4.

• Tilo Schwalger and Anton V Chizhov. Mind the last spike — firing rate models for mesoscopic populations of spiking neurons. Current Opinion in Neurobiology, 58:155–166, October 2019. URL: http://dx.doi.org/10.1016/j.conb.2019.08.003, doi:10.1016/j.conb.2019.08.003.

• Pramod Kumbhar, Michael Hines, Jeremy Fouriaux, Aleksandr Ovcharenko, James King, Fabien Delalondre, and Felix Schürmann. Coreneuron : an optimized compute engine for the neuron simulator. Frontiers in Neuroinformatics, September 2019. URL: http://dx.doi.org/10.3389/fninf.2019.00063, doi:10.3389/fninf.2019.00063.

• Diego Fasoli and Stefano Panzeri. Mathematical studies of the dynamics of finite-size binary neural networks: a review of recent progress. Mathematical Biosciences & Engineering, 16:8025–8059, 01 2019. URL: https://doi.org/10.3934/mbe.2019404, doi:10.3934/mbe.2019404.

• Elena Pastorelli, Cristiano Capone, Francesco Simula, María V. Sánchez-Vives, Paolo Del Giudice, Maurizio Mattia, and Pier Stanislao Paolucci. Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections. Frontiers in Systems Neuroscience, 07 2019. URL: https://doi.org/10.3389/fnsys.2019.00033, doi:10.3389/fnsys.2019.00033.

• Francesco Barchi, Gianvito Urgese, Alessandro Siino, Santa Di Cataldo, Enrico Macii, and Andrea Acquaviva. Flexible on-line reconfiguration of multi-core neuromorphic platforms. IEEE Transactions on Emerging Topics in Computing, 9:915–927, 03 2019. URL: https://doi.org/10.1109/tetc.2019.2908079, doi:10.1109/tetc.2019.2908079.

• Josef Ladenbauer, Sam McKenzie, Daniel F. English, Olivier Hagens, and Srdjan Ostojic. Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nature Communications, 10 2019. URL: https://doi.org/10.1038/s41467-019-12572-0, doi:10.1038/s41467-019-12572-0.

• Robin Gutzen, Michael von Papen, Guido Trensch, Pietro Quaglio, Sonja Grün, and Michael Denker. Reproducible neural network simulations: statistical methods for model validation on the level of network activity data. Frontiers in Neuroinformatics, December 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00090, doi:10.3389/fninf.2018.00090.

• Espen Hagen, Solveig Næss, Torbjørn V. Ness, and Gaute T. Einevoll. Multimodal modeling of neural network activity: computing lfp, ecog, eeg, and meg signals with lfpy 2.0. Frontiers in Neuroinformatics, December 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00092, doi:10.3389/fninf.2018.00092.

• James C. Knight and Thomas Nowotny. Gpus outperform current hpc and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Frontiers in Neuroscience, December 2018. URL: http://dx.doi.org/10.3389/fnins.2018.00941, doi:10.3389/fnins.2018.00941.

• Alexander D. Rast, Samantha V. Adams, Simon Davidson, Sergio Davies, Michael Hopkins, Andrew Rowley, Alan Barry Stokes, Thomas Wennekers, Steve Furber, and Angelo Cangelosi. Behavioral learning in a cognitive neuromorphic robot: an integrative approach. IEEE Transactions on Neural Networks and Learning Systems, 29(12):6132–6144, December 2018. URL: http://dx.doi.org/10.1109/tnnls.2018.2816518, doi:10.1109/tnnls.2018.2816518.

• Corinne Teeter, Ramakrishnan Iyer, Vilas Menon, Nathan Gouwens, David Feng, Jim Berg, Aaron Szafer, Nicholas Cain, Hongkui Zeng, Michael Hawrylycz, Christof Koch, and Stefan Mihalas. Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications, February 2018. URL: http://dx.doi.org/10.1038/s41467-017-02717-4, doi:10.1038/s41467-017-02717-4.

• Johanna Senk, Corto Carde, Espen Hagen, Torsten W. Kuhlen, Markus Diesmann, and Benjamin Weyers. Viola—a multi-purpose and web-based visualization tool for neuronal-network simulation output. Frontiers in Neuroinformatics, November 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00075, doi:10.3389/fninf.2018.00075.

• Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Markus Diesmann, and Sacha Jennifer van Albada. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology, 14(10):e1006359, October 2018. URL: http://dx.doi.org/10.1371/journal.pcbi.1006359, doi:10.1371/journal.pcbi.1006359.

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• Martin Völker, Lukas D.J. Fiederer, Sofie Berberich, Jiří Hammer, Joos Behncke, Pavel Kršek, Martin Tomášek, Petr Marusič, Peter C. Reinacher, Volker A. Coenen, Moritz Helias, Andreas Schulze-Bonhage, Wolfram Burgard, and Tonio Ball. The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial eeg. NeuroImage, 173:564–579, June 2018. URL: http://dx.doi.org/10.1016/j.neuroimage.2018.01.059, doi:10.1016/j.neuroimage.2018.01.059.

• Dayeol Lee, Gwangmu Lee, Dongup Kwon, Sunghwa Lee, Youngsok Kim, and Jangwoo Kim. Flexon: a flexible digital neuron for efficient spiking neural network simulations. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), 275–288. IEEE, June 2018. URL: http://dx.doi.org/10.1109/isca.2018.00032, doi:10.1109/isca.2018.00032.

• missing journal in Fiebig_2018

• Sacha J. van Albada, Andrew G. Rowley, Johanna Senk, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Markus Diesmann, and Steve B. Furber. 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, May 2018. URL: http://dx.doi.org/10.3389/fnins.2018.00291, doi:10.3389/fnins.2018.00291.

• Lealem Mulugeta, Andrew Drach, Ahmet Erdemir, C. A. Hunt, Marc Horner, Joy P. Ku, Jerry G. Myers Jr., Rajanikanth Vadigepalli, and William W. Lytton. Credibility, replicability, and reproducibility in simulation for biomedicine and clinical applications in neuroscience. Frontiers in Neuroinformatics, April 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00018, doi:10.3389/fninf.2018.00018.

• Nicolas P. Rougier. A density-driven method for the placement of biological cells over two-dimensional manifolds. Frontiers in Neuroinformatics, March 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00012, doi:10.3389/fninf.2018.00012.

• Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, and Susanne Kunkel. Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers. Frontiers in Neuroinformatics, February 2018. URL: http://dx.doi.org/10.3389/fninf.2018.00002, doi:10.3389/fninf.2018.00002.

• Joanna Urban-Ciecko, Jean-Sebastien Jouhanneau, Stephanie E. Myal, James F.A. Poulet, and Alison L. Barth. Precisely timed nicotinic activation drives sst inhibition in neocortical circuits. Neuron, 97(3):611–625.e5, February 2018. URL: http://dx.doi.org/10.1016/j.neuron.2018.01.037, doi:10.1016/j.neuron.2018.01.037.

• Pablo Martínez-Cañada, Milad Hobbi Mobarhan, Geir Halnes, Marianne Fyhn, Christian Morillas, Francisco Pelayo, and Gaute T. Einevoll. Biophysical network modeling of the dlgn circuit: effects of cortical feedback on spatial response properties of relay cells. PLOS Computational Biology, 14(1):e1005930, January 2018. URL: http://dx.doi.org/10.1371/journal.pcbi.1005930, doi:10.1371/journal.pcbi.1005930.

• Gianvito Urgese, Francesco Barchi, Enrico Macii, and Andrea Acquaviva. Optimizing network traffic for spiking neural network simulations on densely interconnected many-core neuromorphic platforms. IEEE Transactions on Emerging Topics in Computing, 6(3):317–329, July 2018. URL: http://dx.doi.org/10.1109/tetc.2016.2579605, doi:10.1109/tetc.2016.2579605.

• Maximilian Schmidt, Rembrandt Bakker, Claus C. Hilgetag, Markus Diesmann, and Sacha J. van Albada. Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function, 223(3):1409–1435, November 2017. URL: http://dx.doi.org/10.1007/s00429-017-1554-4, doi:10.1007/s00429-017-1554-4.

• Nobuhiko Wagatsuma and Ko Sakai. Modeling the time-course of responses for the border ownership selectivity based on the integration of feedforward signals and visual cortical interactions. Frontiers in Psychology, January 2017. URL: http://dx.doi.org/10.3389/fpsyg.2016.02084, doi:10.3389/fpsyg.2016.02084.

• Clément Vitrac and Marianne Benoit-Marand. Monoaminergic modulation of motor cortex function. Frontiers in Neural Circuits, October 2017. URL: http://dx.doi.org/10.3389/fncir.2017.00072, doi:10.3389/fncir.2017.00072.

• Lorenzo Vannucci, Egidio Falotico, and Cecilia Laschi. Proprioceptive feedback through a neuromorphic muscle spindle model. Frontiers in Neuroscience, June 2017. URL: http://dx.doi.org/10.3389/fnins.2017.00341, doi:10.3389/fnins.2017.00341.

• Hesam Setareh, Moritz Deger, Carl C. H. Petersen, and Wulfram Gerstner. Cortical dynamics in presence of assemblies of densely connected weight-hub neurons. Frontiers in Computational Neuroscience, June 2017. URL: http://dx.doi.org/10.3389/fncom.2017.00052, doi:10.3389/fncom.2017.00052.

• Tilo Schwalger, Moritz Deger, and Wulfram Gerstner. Towards a theory of cortical columns: from spiking neurons to interacting neural populations of finite size. PLOS Computational Biology, 13(4):e1005507, April 2017. URL: http://dx.doi.org/10.1371/journal.pcbi.1005507, doi:10.1371/journal.pcbi.1005507.

• Jannis Schuecker, Maximilian Schmidt, Sacha J. van Albada, Markus Diesmann, and Moritz Helias. Fundamental activity constraints lead to specific interpretations of the connectome. PLOS Computational Biology, 13(2):e1005179, February 2017. URL: http://dx.doi.org/10.1371/journal.pcbi.1005179, doi:10.1371/journal.pcbi.1005179.

• Christopher L. Rees, Keivan Moradi, and Giorgio A. Ascoli. Weighing the evidence in peters’ rule: does neuronal morphology predict connectivity? Trends in Neurosciences, 40(2):63–71, February 2017. URL: http://dx.doi.org/10.1016/j.tins.2016.11.007, doi:10.1016/j.tins.2016.11.007.

• Jung Hoon Lee and Stefan Mihalas. Visual processing mode switching regulated by vip cells. Scientific Reports, May 2017. URL: http://dx.doi.org/10.1038/s41598-017-01830-0, doi:10.1038/s41598-017-01830-0.

• Jung H. Lee, Christof Koch, and Stefan Mihalas. A computational analysis of the function of three inhibitory cell types in contextual visual processing. Frontiers in Computational Neuroscience, April 2017. URL: http://dx.doi.org/10.3389/fncom.2017.00028, doi:10.3389/fncom.2017.00028.

• Tim Kunze, Andre D. H. Peterson, Jens Haueisen, and Thomas R. Knösche. A model of individualized canonical microcircuits supporting cognitive operations. PLOS ONE, 12(12):e0188003, December 2017. URL: http://dx.doi.org/10.1371/journal.pone.0188003, doi:10.1371/journal.pone.0188003.

• Tammo Ippen, Jochen M. Eppler, Hans E. Plesser, and Markus Diesmann. Constructing neuronal network models in massively parallel environments. Frontiers in Neuroinformatics, May 2017. URL: http://dx.doi.org/10.3389/fninf.2017.00030, doi:10.3389/fninf.2017.00030.

• Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias Bolten, Moritz Helias, and Markus Diesmann. Integration of continuous-time dynamics in a spiking neural network simulator. Frontiers in Neuroinformatics, May 2017. URL: http://dx.doi.org/10.3389/fninf.2017.00034, doi:10.3389/fninf.2017.00034.

• Mauricio Girardi-Schappo, Germano S. Bortolotto, Rafael V. Stenzinger, Jheniffer J. Gonsalves, and Marcelo H. R. Tragtenberg. Phase diagrams and dynamics of a computationally efficient map-based neuron model. PLOS ONE, 12(3):e0174621, March 2017. URL: http://dx.doi.org/10.1371/journal.pone.0174621, doi:10.1371/journal.pone.0174621.

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• Espen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Lindén, Tom Tetzlaff, Sacha J. van Albada, Sonja Grün, Markus Diesmann, and Gaute T. Einevoll. Hybrid scheme for modeling local field potentials from point-neuron networks. Cerebral Cortex, 26(12):4461–4496, October 2016. URL: http://dx.doi.org/10.1093/cercor/bhw237, doi:10.1093/cercor/bhw237.

• Chris Gaiteri, Sara Mostafavi, Christopher J. Honey, Philip L. De Jager, and David A. Bennett. Genetic variants in alzheimer disease — molecular and brain network approaches. Nature Reviews Neurology, 12(7):413–427, June 2016. URL: http://dx.doi.org/10.1038/nrneurol.2016.84, doi:10.1038/nrneurol.2016.84.

• Salvador Dura-Bernal, Kan Li, Samuel A. Neymotin, Joseph T. Francis, Jose C. Principe, and William W. Lytton. Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Frontiers in Neuroscience, February 2016. URL: http://dx.doi.org/10.3389/fnins.2016.00028, doi:10.3389/fnins.2016.00028.

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• Hannah Bos, Markus Diesmann, and Moritz Helias. Identifying anatomical origins of coexisting oscillations in the cortical microcircuit. PLOS Computational Biology, 12(10):e1005132, October 2016. URL: http://dx.doi.org/10.1371/journal.pcbi.1005132, doi:10.1371/journal.pcbi.1005132.

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• Simon Friedmann, Johannes Schemmel, Andreas Grübl, Andreas Hartel, Matthias Hock, and Karlheinz Meier. Demonstrating hybrid learning in a flexible neuromorphic hardware system. IEEE Transactions on Biomedical Circuits and Systems, 11:128–142, 09 2016. URL: https://doi.org/10.1109/tbcas.2016.2579164, doi:10.1109/tbcas.2016.2579164.

• Sacha Jennifer van Albada, Moritz Helias, and Markus Diesmann. Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. PLOS Computational Biology, 11(9):e1004490, September 2015. URL: http://dx.doi.org/10.1371/journal.pcbi.1004490, doi:10.1371/journal.pcbi.1004490.

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• Jannis Schuecker, Markus Diesmann, and Moritz Helias. Modulated escape from a metastable state driven by colored noise. Physical Review E, November 2015. URL: http://dx.doi.org/10.1103/PhysRevE.92.052119, doi:10.1103/physreve.92.052119.

• Alberto Mazzoni, Henrik Lindén, Hermann Cuntz, Anders Lansner, Stefano Panzeri, and Gaute T. Einevoll. Computing the local field potential (lfp) from integrate-and-fire network models. PLOS Computational Biology, 11(12):e1004584, December 2015. URL: http://dx.doi.org/10.1371/journal.pcbi.1004584, doi:10.1371/journal.pcbi.1004584.

• Sarah Jarvis and Simon R. Schultz. Prospects for optogenetic augmentation of brain function. Frontiers in Systems Neuroscience, November 2015. URL: http://dx.doi.org/10.3389/fnsys.2015.00157, doi:10.3389/fnsys.2015.00157.

• Jan Hahne, Moritz Helias, Susanne Kunkel, Jun Igarashi, Matthias Bolten, Andreas Frommer, and Markus Diesmann. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics, September 2015. URL: http://dx.doi.org/10.3389/fninf.2015.00022, doi:10.3389/fninf.2015.00022.

• Dean R. Freestone, Philippa J. Karoly, Andre D. H. Peterson, Levin Kuhlmann, Alan Lai, Farhad Goodarzy, and Mark J. Cook. Seizure prediction: science fiction or soon to become reality? Current Neurology and Neuroscience Reports, September 2015. URL: http://dx.doi.org/10.1007/s11910-015-0596-3, doi:10.1007/s11910-015-0596-3.

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• Frederik Beuth and Fred H. Hamker. A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. Vision Research, 116:241–257, November 2015. URL: http://dx.doi.org/10.1016/j.visres.2015.04.004, doi:10.1016/j.visres.2015.04.004.

• Richard J. Tomsett, Matt Ainsworth, Alexander Thiele, Mehdi Sanayei, Xing Chen, Marc A. Gieselmann, Miles A. Whittington, Mark O. Cunningham, and Marcus Kaiser. Virtual electrode recording tool for extracellular potentials (vertex): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue. Brain Structure and Function, 220(4):2333–2353, May 2014. URL: http://dx.doi.org/10.1007/s00429-014-0793-x, doi:10.1007/s00429-014-0793-x.

• Petar Tomov, Rodrigo F. O. Pena, Michael A. Zaks, and Antonio C. Roque. Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types. Frontiers in Computational Neuroscience, September 2014. URL: http://dx.doi.org/10.3389/fncom.2014.00103, doi:10.3389/fncom.2014.00103.

• Qiusong Tang, Michael Brecht, and Andrea Burgalossi. Juxtacellular recording and morphological identification of single neurons in freely moving rats. Nature Protocols, 9(10):2369–2381, September 2014. URL: http://dx.doi.org/10.1038/nprot.2014.161, doi:10.1038/nprot.2014.161.

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• Günther Palm, Andreas Knoblauch, Florian Hauser, and Almut Schüz. Cell assemblies in the cerebral cortex. Biological Cybernetics, 108(5):559–572, April 2014. URL: http://dx.doi.org/10.1007/s00422-014-0596-4, doi:10.1007/s00422-014-0596-4.

• Moritz Helias, Tom Tetzlaff, and Markus Diesmann. The correlation structure of local neuronal networks intrinsically results from recurrent dynamics. PLoS Computational Biology, 10(1):e1003428, January 2014. URL: http://dx.doi.org/10.1371/journal.pcbi.1003428, doi:10.1371/journal.pcbi.1003428.

• Markus Butz, Ines D. Steenbuck, and Arjen van Ooyen. Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke. Frontiers in Neuroanatomy, October 2014. URL: http://dx.doi.org/10.3389/fnana.2014.00115, doi:10.3389/fnana.2014.00115.

• Markus Butz, Ines Derya Steenbuck, and Arjen van Ooyen. Homeostatic structural plasticity increases the efficiency of small-world networks. Frontiers in Synaptic Neuroscience, 04 2014. URL: https://doi.org/10.3389/fnsyn.2014.00007, doi:10.3389/fnsyn.2014.00007.

• Nobuhiko Wagatsuma, Tobias C. Potjans, Markus Diesmann, Ko Sakai, and Tomoki Fukai. Spatial and feature-based attention in a layered cortical microcircuit model. PLoS ONE, 8(12):e80788, December 2013. URL: http://dx.doi.org/10.1371/journal.pone.0080788, doi:10.1371/journal.pone.0080788.

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• Nikola T. Markov, Mária Ercsey-Ravasz, David C. Van Essen, Kenneth Knoblauch, Zoltán Toroczkai, and Henry Kennedy. Cortical high-density counterstream architectures. Science, November 2013. URL: http://dx.doi.org/10.1126/science.1238406, doi:10.1126/science.1238406.

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• Ján Antolík and Andrew P. Davison. Integrated workflows for spiking neuronal network simulations. Frontiers in Neuroinformatics, 2013. URL: http://dx.doi.org/10.3389/fninf.2013.00034, doi:10.3389/fninf.2013.00034.

• M Helias, T Tetzlaff, and M Diesmann. Echoes in correlated neural systems. New Journal of Physics, 15(2):023002, February 2013. URL: http://dx.doi.org/10.1088/1367-2630/15/2/023002, doi:10.1088/1367-2630/15/2/023002.

• Gaute T. Einevoll, Christoph Kayser, Nikos K. Logothetis, and Stefano Panzeri. Modelling and analysis of local field potentials for studying the function of cortical circuits. Nature reviews. Neuroscience, 14:770–785, 10 2013. URL: https://doi.org/10.1038/nrn3599, doi:10.1038/nrn3599.