Parallel network simulations with NEURON

The NEURON Simulation Environment

Hines, M. L., & Carnevale, N. T. (1997) · Neural Computation · doi:10.1162/neco.1997.9.6.1179 · Cited by 2,630

Multilevelk-way Partitioning Scheme for Irregular Graphs

Karypis, G., & Kumar, V. (1998) · Journal of Parallel and Distributed Computing · doi:10.1006/jpdc.1997.1404 · Cited by 1,693

Linda in context

Carriero, N. J., & Gelernter, D. H. (1989) · Communications of the ACM · doi:10.1145/63334.63337 · Cited by 1,405

The Blue Brain Project

Markram, H. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1848 · Cited by 1,227

Role of an A-Type K+ Conductance in the Back-Propagation of Action Potentials in the Dendrites of Hippocampal Pyramidal Neurons

Migliore, M., Hoffman, D. A., Magee, J. C., & Johnston, D. (1999) · Journal of Computational Neuroscience · doi:10.1023/a:1008906225285 · Cited by 334

Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: a network model of the dentate gyrus incorporating cell types and axonal topography

Santhakumar, V., Aradi, I., & Soltesz, I. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00777.2004 · Cited by 262

Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing

Morrison, A., Mehring, C., Geisel, T., Aertsen, A., & Diesmann, M. (2005) · Neural Computation · doi:10.1162/0899766054026648 · Cited by 201

Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

Mattia, M., & Giudice, P. D. (2000) · Neural Computation · doi:10.1162/089976600300014953 · Cited by 164

Optimization of MPI collective communication on BlueGene/L systems

Almasi, G. S., Heidelberger, P., Archer, C. J., Martorell, X., Erway, C. C., Moreira, J. E., Steinmacher-Burow, B., & Zheng, Y. (2005) · International Conference on Supercomputing · doi:10.1145/1088149.1088183 · Cited by 160

SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons

Delorme, A., & Thorpe, S. J. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/4/301 · Cited by 98

NeMo

Plagge, M., Carothers, C. D., & Gonsiorowski, E. J. (2016) · ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation (PADS) · doi:10.1145/2901378.2901392 · Cited by 10

MODEL-BASED NEUROIMAGING FOR COGNITIVE COMPUTING

Poznański, R. R. (2009) · Journal of Integrative Neuroscience · doi:10.1142/s021963520900223x · Cited by 10

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements

Fruzzetti, L., Kalidindi, H. T., Antonietti, A., Alessandro, C., Geminiani, A., Casellato, C., Falotico, E., & D'Angelo, E. U. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010564 · Cited by 10

Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models

Geit, W. A. H. V., Achard, P., & Schutter, E. D. (2007) · BMC Neuroscience · doi:10.1186/1471-2202-8-s2-p5 · Cited by 10

Leveraging a Cluster-Booster Architecture for Brain-Scale Simulations

Kumbhar, P., Hines, M. L., Ovcharenko, A., Mallon, D. A., King, J. G., Sainz, F., Schürmann, F., & Delalondre, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41321-1_19 · Cited by 10

NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules

Linssen, C., Babu, P. N., Eppler, J. M., Koll, L., Rumpe⋆, B., & Morrison, A. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1544143 · Cited by 9

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Kumbhar, P., Awile, O., Keegan, L., Alonso, J. B., King, J. G., Hines, M. L., & Schürmann, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50371-0_4 · Cited by 9

Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models

Lee, G., Matsunaga, A., Durá-Bernal, S., Zhang, W., Lytton, W. W., Francis, J. T., & Fortes, J. A. B. (2014) · Journal of Computational Surgery · doi:10.1186/s40244-014-0012-3 · Cited by 9

Automating NEURON Simulation Deployment in Cloud Resources

Stockton, D. B., & Santamarı́a, F. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9315-8 · Cited by 9

Communication Sparsity in Distributed Spiking Neural Network Simulations to Improve Scalability

Musoles, C. F., Coca, D., & Richmond, P. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00019 · Cited by 9

A multi‐GPU algorithm for large‐scale neuronal networks

Camargo, R. Y. D., Rozante, L. C. S., & Song, S. W. (2010) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.1665 · Cited by 9

Advancements in High-Performance Computing in Early Diagnosis of Alzheimer’s Disease: A Systematic Review

Kirimtat, A., Krejcar, O., Marešová, P., Selamat, A., & Kuča, K. (2024) · IEEE Access · doi:10.1109/access.2024.3484928 · Cited by 8

Efficiently passing messages in distributed spiking neural network simulation

Thibeault, C. M., Minkovich, K., O'Brien, M. J., Harris, F. C., & Srinivasa, N. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00077 · Cited by 8

Information coding in a laminar computational model of cat primary visual cortex

Basalyga, G., Montemurro, M. A., & Wennekers, T. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0420-x · Cited by 8

Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials

Parasuram, H., Nair, B. G., Naldi, G., D'Angelo, E. U., & Diwakar, S. (2017) · Annals of Neurosciences · doi:10.1159/000481905 · Cited by 8

Computational Concepts for Reconstructing and Simulating Brain Tissue

Schürmann, F., Courcol, J.-D., & Ramaswamy, S. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_10 · Cited by 7

An event-driven model for the SpiNNaker virtual synaptic channel

Rast, A. D., Galluppi, F., Davies, S., Plana, L. A., Sharp, T., & Furber, S. B. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033466 · Cited by 7

Data Mining Through Simulation

Lytton, W. W., & Stewart, M. (2007) · Methods in molecular biology · doi:10.1007/978-1-59745-520-6_9 · Cited by 7

NEURON Simulation Environment

Hines, M. L., & Carnevale, T. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_795-1 · Cited by 7

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

Vanni, S., Sharifian, F., Heikkinen, H., & Vigário, R. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00332.2014 · Cited by 7

Optimizing NEURON brain simulator with Remote Memory Access on distributed memory systems

Shehzad, D., & Bozkuş, Z. (2015) · International Conference on Emerging Technologies · doi:10.1109/icet.2015.7389167 · Cited by 6

Growing a garden of neurons

Evans, R. C., & Polavaram, S. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00017 · Cited by 6

Framework for Interactive Million-Neuron Simulation

Hereld, M., Stevens, R. L., Lee, H. C., & van Drongelen, W. (2007) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3180337703 · Cited by 6

First impressions from detailed brain model simulations on a Xeon/Xeon-Phi node

Chatzikonstantis, G., Rodopoulos, D., Nomikou, S., Strydis, C., Zeeuw, C. I. D., & Soudris, D. (2016) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/2903150.2903477 · Cited by 5

Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation

Carvalho, N. A., Contassot‐Vivier, S., Buhry, L., & Martinez, D. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.522000 · Cited by 5

The Role of Brain Signal Processing and Neuronal Modelling in Epilepsy – A Review

Najafi, T., Jaafar, R., Remli, R., Zaidi, A. W., & Chellappan, K. (2021) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2021-33(4)-03 · Cited by 5

PERIODIC ORBITS OF SINGLE NEURON MODELS WITH INTERNAL DECAY RATE 0 < Β ≤ 1

Anisimova, A., Avotiņa, M., & Bula, I. (2013) · Mathematical Modelling and Analysis · doi:10.3846/13926292.2013.804462 · Cited by 5

On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks

Kaabi, M. G., Arnaud Tonnelier, S., & Martinez, D. (2011) · Neural Computation · doi:10.1162/neco_a_00112 · Cited by 5

Simulation of Large Networks

Lytton, W. W., Stewart, M., & Hines, M. L. (2008) · Elsevier eBooks · doi:10.1016/b978-012373649-9.50004-1 · Cited by 6

Massively parallel neural computation

Fox, P. J. (2013) · Apollo (University of Cambridge) · doi:10.17863/cam.16380 · Cited by 5

Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium

Srikanth, S., & Narayanan, R. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1241450 · Cited by 4

ATM: Area-based Partition and Topology-aware Mapping for Large-scale SNN Simulation

Zeng, Y., Feng, G., Chen, Z., Lu, Y., & Xiao, N. (2024) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa63168.2024.00251 · Cited by 4

A novel parallel clock-driven algorithm for simulation of neuronal networks based on virtual synapse

Wang, Z., Xia, P., Han, F., & Song, G. (2020) · SIMULATION · doi:10.1177/0037549720903804 · Cited by 4

A novel simulator for extended Hodgkin-Huxley neural networks

Panagiotou, S., Miedema, R., Sidiropoulos, H., Smaragdos, G., Strydis, C., & Soudris, D. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00071 · Cited by 4

Carbon Nanotubes as Electrical Interfaces to Neurons

Giugliano, M., Gambazzi, L., Ballerini, L., Prato, M., & Campidelli, S. (2011) · Fundamental biomedical technologies · doi:10.1007/978-0-387-31296-5_9 · Cited by 4

Neurospaces:Towards automated model partitioning for parallel computers

Cornelis, H., & Schutter, E. D. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.140 · Cited by 4

A biophysical regulator of inhibitory integration and learning in mesolimbic dopamine neuronsPreprint

Costa, K. M., Hammer, N., Knowlton, C. J., Schwenk, J., Müller, T., Schulte, D., Fakler, B., Canavier, C. C., & Roeper, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/344499 · Cited by 4

Large-scale network simulation over heterogeneous computing architecture

Romdhanne, B. B. (2013) · doi:10.70675/eec34495zfc63z42eez866bz6197e4eb5edb · Cited by 4

Analysis model of energy consumption variables for data processing in high-performance computing systems

Arandia, J. L., Gómez, C. J. V., Montoya, L. A. A., & Dueñas, V. L. R. (2024) · Revista Colombiana de Computación · doi:10.29375/25392115.5058 · Cited by 3

TVB C++: A Fast and Flexible Back‐End for The Virtual Brain

Martín, I., Zamora‐López, G., Fousek, J., Schirner, M., Ritter, P., Jirsa, V., Deco, G., & Patow, G. (2025) · Advanced Science · doi:10.1002/advs.202406440 · Cited by 4

Advanced Tutorial: Parallel and Distributed Methods for Scalable Discrete Simulation

Andelfinger, P., & Cai, W. (2022) · Winter Simulation Conference · doi:10.1109/wsc57314.2022.10015291 · Cited by 3

Stability Analysis of Implicit Finite-Difference Schemes for Parabolic Problems on Graphs

Čiegis, R., & Tumanova, N. (2011) · Numerical Functional Analysis and Optimization · doi:10.1080/01630563.2011.626886 · Cited by 3

Real-time Utilization of System-scale Neuroscience Models

Morén, J., Sugimoto, N., & Doya, K. (2015) · The Brain & Neural Networks · doi:10.3902/jnns.22.125 · Cited by 3

High performance, large-scale multi-compartment Hodgkin-Huxley simulation of Drosophila’s whole-brain neural circuit modelPreprint

Higuchi, K., Kazawa, T., Sakai, B., Namiki, S., Haupt, S. S., & Kanzaki, R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.01.512969 · Cited by 3

Theta resonance and synaptic modulation scale activity patterns in the medial entorhinal cortex stellate cells

Katyare, N., & Sikdar, S. K. (2020) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14434 · Cited by 2

Adapting CMA-ES for the Massively Parallelized Simulation of Neurons and Neural Circuits

Kazawa, T., Fukuda, T., Goto, A., Miyamoto, D., Haupt, S. S., & Kanzaki, R. (2019) · DEStech Transactions on Engineering and Technology Research · doi:10.12783/dtetr/amsms2019/31846 · Cited by 2

The neuronal response at extended timescales: a linearized spiking input–output relation

Soudry, D., & Meir, R. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00029 · Cited by 2

A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures

Kublik, R. A., & Chopp, D. L. (2016) · Advances in Computational Mathematics · doi:10.1007/s10444-015-9437-9 · Cited by 2

Single-Neuron and Network Computation in Realistic Models of the Cerebellar Cortex

D'Angelo, E. U., Masoli, S., Rizza, M. F., & Casali, S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801386-1.00011-3 · Cited by 3

The Neuron Simulation Environment in Epilepsy Research

Carnevale, N. T., & Hines, M. L. (2008) · Elsevier eBooks · doi:10.1016/b978-012373649-9.50005-3 · Cited by 2

MPI Parallelization of NEUROiD Models Using Docker Swarm

Iyengar, R. S., & Raghavan, M. (2020) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads51040.2020.00092 · Cited by 2

Self-construction in the context of cortical growth: from one cell to a cortex to a programming paradigm for self-constructing systems

Hauri, A. (2013) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-009997273 · Cited by 3

Distributed Heterogeneous Spiking Neural Network Simulator Using Sunway Accelerators

Li, X., Wang, Z., Pan, Y. F., Meng, J., Feng, S., & Wei, Y. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020007 · Cited by 1

From data to discovery: Neuroinformatics in understanding Alzheimer's disease

Pahal, S., Pahal, V., & Chaudhary, A. (2024) · Journal of Biosciences · doi:10.1007/s12038-024-00486-z · Cited by 1

Study of Neocortex Simulations with GENESIS on High Performance Computing Resources

McDaniel, S., Boothe, D. L., Crone, J. C., Park, S. J., Shires, D. R., Yu, A. B., & Taufer, M. (2016) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2016.0124 · Cited by 1

Perspective: an optoelectronic future for heterogeneous, dendritic computing

Srouji, L. E., Abdelghany, M., Ambethkar, H. R., Lee, Y.-J., On, M. B., & Yoo, S. J. B. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1394271 · Cited by 2

Computer Simulation Environments

Gleeson, P., Silver, R. A., & Steuber, V. (2010) · Springer eBooks · doi:10.1007/978-1-4419-0996-1_21 · Cited by 1

Elements realization of software-algorithmic system optimization of technological processes based on adaptive methods

Rutskov, A. L., Sidorenko, E. V., Burkovsky, V. L., & Fedorov, Y. P. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/791/1/012034 · Cited by 1

System design and implementation of a fast and accurate bio-inspired spiking neural network

Wang, Z.-Z. (2015) · doi:10.25148/etd.fidc000072 · Cited by 1

Computational Neuroscience – Biophysical Modeling of Neural Systems

Stratton, H. J., & Si, J. (2015) · Springer eBooks · doi:10.1007/978-3-662-43505-2_34

Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience

Tumanova, N., & Čiegis, R. (2014) · doi:10.1002/9781118711897.ch4

Using Neuroconstruct to Develop and Modify Biologically Detailed 3D Neuronal Network Models in Health and Disease

Gleeson, P., Steuber, V., & Silver, R. A. (2008) · Elsevier eBooks · doi:10.1016/b978-012373649-9.50008-9

Time Scale Analysis of Neuronal Ensemble Data Used to Feed Neural Network Models

de Vasconcelos, N. A. P., Blanco, W., Faber, J., Gomes, H. M., de Barros, T. M., & Ribeiro, S. T. G. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0724-9_10

Spiking input-output relation for general biophysical neuron modelsPreprint

Soudry, D., & Meir, R. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.6251 · Cited by 1

Parallel Algorithms for Parabolic Problems on Graphs

Čiegis, R., & Tumanova, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31500-8_34

NCS: Neuron Models, User Interface, and Modeling

Tanna, D. (2014)

Transforming the BCPNN Learning Rule for Spiking Units to a Learning Rule for Non-Spiking Units

Bergel, A. (2010)

Parallelizing large networks using NEURON-Python

Seidenstein, A. H., McDougal, R. A., Hines, M. L., & Lytton, W. W. (2015) · BMC Neuroscience · doi:10.1186/1471-2202-16-s1-p151

Model reduction of large spiking neurons

Kellems, A. R. (2010) · Rice Research Repository (Rice University)

Framework for Interactive Million-Neuron Simulation Running title: Interactive Million Neuron Simulation

Hereld, M., Stevens, R. L., van Drongelen, W., & Lee, H. C. (2006)

Architectures for communication between processes and software layers for a simulator for biological neural networks.

Eppler, J. M. (2010) · FreiDok plus (Universitätsbibliothek Freiburg)

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

Shehzad, D., & Bozkuş, Z. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/3676582

Propagation of Seizurelike Activity in a Model of Neocortex Running title: Seizurelike Activity in a Model of Neocortex

van Drongelen, W., Lee, H. C., Stevens, R. L., & Hereld, M. (2006)

A bi-directional communication paradigm between parallel NEURON and an external non-neuron process

Hendrickson, P. J., Bingham, C. S., Song, D. B., & Berger, T. W. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590973

NCS: Neuron Models, User Interface, and

Tanna, D. (2014)

Analysis, Tuning and Implementation of a Hippocampal CA1 Microcircuit

Cuomo, S., Michele, P. D., & Galletti, A. (2016) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814759717_0022

Knowledge Representation and Data Mining of Neuronal Morphologies Using Neuroinformatics Tools and Formal Ontologies

Polavaram, S. (2017) · doi:10.13021/mars/7038

A Biologically Plausible Supervised Learning Method for Spiking Neurons with Real-world Applications

Guo, L. (2016) · doi:10.25148/etd.fidc001243

A computational model of spatial encoding in the hippocampus

Schönfeld, F. (2016) · Dokumentenrepositorium der RUB (Ruhr University Bochum)

introduction to the Neural Query System

Lytton, W. W., & Stewart, M. (2009)

Telling neuronal apples from oranges: analytical performance modeling of neural tissue simulationsPreprint

Cremonesi, F., & Schürmann, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02757

Implementation of Syncytial Models in NEURON Simulator for Improved Efficiency

Appukuttan, S., Mandge, D., & Manchanda, R. (2020) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp50117.2020.00048

Technical and economic optimization of fuel facilities – energy complex using elements of fuzzy logic and artificial neural networks

Rutskov, A. L., Fedorov, Y. P., & Volkov, L. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1089/1/012031

N-Neuron Simulation Using Multiprocessor Cluster

Shanka, D. S. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-93709-6_23

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and vigor of saccadic eye movementsPreprint

Fruzzetti, L., Kalidindi, H. T., Antonietti, A., Alessandro, C., Geminiani, A., Casellato, C., Falotico, E., & D'Angelo, E. U. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.483471

Large scale multiphysics modeling of neurites

García-Grajales, J. A. (2014) · doi:10.20868/upm.thesis.32191

NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON

Cobb, E., Petroccione, M. A., & Scimemi, A. (2023) · Scientific Reports · doi:10.1038/s41598-022-27302-8

On Scaling Neuronal Network Simulations Using Distributed Computing

Omelyusik, V., Shehzad, K., Banks, T., Rao, P. R., & Nair, S. S. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00105

A NEST-based framework unlocks massively parallel simulation of networks of multi-compartment neurons with customizable subcellular dynamics

Wybo, W. A. M., Ewert, L., Linssen, C., Babu, P. N., Pastorelli, E., Paolucci, P. S., & Morrison, A. (2026) · Communications Biology · doi:10.1101/2025.06.30.662287

Netiesinių matematinių modelių grafuose skaitinė analizė

Tumanova, N. (2012) · doi:10.20334/2006-m

Exploiting network topology in brain-scale simulations of spiking neural networks

Lober, M., Diesmann, M., & Kunkel, S. (2026) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ae762e · Cited by 3

KChIP4a is a Biophysical Amplifier of Inhibition in Atypical Dopamine Neurons and Controls Learning from Negative Prediction Errors

Costa, K. M., Hammer, N., Knowlton, C. J., Schwenk, J., Müller, T., Schulte, D., Fakler, B., Canavier, C. C., & Roeper, J. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1956-25.2026 · Cited by 4

Automating population construction and parallel simulation of biophysical models for neuromuscular cells: An inverse approach

Kim, H. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014184

UPAYA PEMERINTAH AMERIKA SERIKAT (AS) MELALUI CISA DALAM MENANGGULANGI SERANGAN SIBER TERHADAP SOLARWINDS OLEH RUSIA TAHUN 2020-2021

Rafi, M. (2026) · Universitas Pasundan institutional repositories & scientific journals (Universitas Pasundan)

Scalable Event-Driven Modelling Architectures for Neuromimetic Hardware

Rast, A. D. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique)

Action Editor: Alain Destexhe

Neurosci, J. C., Migliore, M., Cannia, C., Lytton, W. W., Migliore, M., Cannia, C., Migliore, M., Cannia, C., Lytton, W. W., Markram, H., et al. (2015)

POSTER PRESENTATION Open Access Parallelizing large networks using NEURON-Python

Seidenstein, R. H., McDougal, R. A., Hines, M. L., & Lytton, W. W. (2016)

Simulating neurons and networks on parallel computers

Nelson, M. E., Furmanski, W., & Bower, J. M. (1989) · Cited by 14

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON

Lytton, W. W., Seidenstein, A. H., Durá-Bernal, S., McDougal, R. A., Schürmann, F., & Hines, M. L. (2016) · Neural Computation · doi:10.1162/neco_a_00876 · Cited by 43

PARALLEL NEUROSYS: A system for the simulation of very large networks of biologically accurate neurons on parallel computers

Pacheco, P. S., Camperi, M., & Uchino, T. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00283-6 · Cited by 10