The virtual NEST Conference 2022

Thursday/Friday 23/24 June

The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the virtual NEST Conference 2022. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application.

The NEST Conference 2022 will be held as a virtual conference on Thursday/Friday 23/24 June.

Registration is open!

Please note that you can profit from a reduced fee by becoming a NEST Initiative member before April 30! For more information on conference and membership fees please visit the conference page.

NEST Initiative

The Neural Simulation Technology Initiative

The NEST Initiative has advanced computational neuroscience since 2001 by pushing the limits of large-scale simulations of biologically realistic neuronal networks. Since 2012, the NEST Initiative is incorporated as a non-profit member-based organization promoting scientfic collaboration in computational neuroscience.

The Board and Members govern the NEST Initiative in accordance to its Statutes.

What we do

As a community of developers:

  • We coordinate and guide the development of the NEST Simulator.
  • We regularly publish on simulation technology, data structures and algorithms for large-scale neuronal network simulation:

    Latest Publications

    • [DOI] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M and Kunkel S (2018) Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics
    • [DOI] Krishnan J, Porta Mana P, Helias M, Diesmann M and Di Napoli E (2018) Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. Frontiers in Neuroinformatics
    • [DOI] Ippen T, Eppler JM., Plesser HE and Diesmann M (2017) Constructing neuronal network models in massively parallel environments. Front. Neuroinform.
      		  Title                    = {Constructing neuronal network models in massively parallel environments},
      		  Author                   = {Ippen, Tammo and Eppler, Jochen M. and Plesser, Hans Ekkehard and Diesmann, Markus},
      		  Journal                  = {Front. Neuroinform.},
      		  Year                     = {2017},
      		  Abstract                 = {Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.},
      		  Doi                      = {10.3389/fninf.2017.00030},
      		  Nest_category            = {nest_technology},
      		  Owner                    = {steffen},
      		  Timestamp                = {2017.05.08},
      		  Url                      = {}

    • [DOI] Plesser H, Diesmann M, Gewaltig M, Morrison A (2015) Nest: the neural simulation tool. In Encyclopedia of computational neuroscience, ed. Jaeger D Jung R 1849-1852Springer New York. .
      Title = {NEST: the Neural Simulation Tool},
      Author = {Plesser, HansEkkehard and Diesmann, Markus and Gewaltig,
      Marc-Oliver and Morrison, Abigail},
      Booktitle = {Encyclopedia of Computational Neuroscience},
      Publisher = {Springer New York},
      Year = {2015},
      Editor = {Jaeger, Dieter and Jung, Ranu},
      Pages = {1849-1852},
      Doi = {10.1007/978-1-4614-6675-8_258},
      ISBN = {978-1-4614-6674-1},
      Language = {English},
      Nest_category = {nest_technology},
      Owner = {steffen},
      Timestamp = {2015.03.24},
      Url = {}

    • [DOI] Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M (2015) A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in neuroinformatics 9:22.
      Title = {A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations},
      Author = {Hahne, Jan and Helias, Moritz and Kunkel, Susanne and Igarashi, Jun and Bolten, Matthias and Frommer, Andreas and Diesmann, Markus},
      Journal = {Frontiers in Neuroinformatics},
      Year = {2015},
      Number = {22},
      Volume = {9},
      Abstract = {Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.},
      Doi = {10.3389/fninf.2015.00022},
      ISSN = {1662-5196},
      Nest_category = {nest_technology},
      Owner = {steffen},
      Timestamp = {2015.09.09},
      Url = {}

    • [DOI] Kunkel S, Schmidt M, Eppler J M, Plesser H E, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M, Helias M (2014) Spiking network simulation code for petascale computers. Frontiers in neuroinformatics 8:78.
      Title = {Spiking network simulation code for petascale computers},
      Author = {Kunkel, Susanne and Schmidt, Maximilian and Eppler, Jochen Martin and Plesser, Hans Ekkehard and Masumoto, Gen and Igarashi, Jun and Ishii, Shin and Fukai, Tomoki and Morrison, Abigail and Diesmann, Markus and Helias, Moritz},
      Journal = {Frontiers in Neuroinformatics},
      Year = {2014},
      Number = {78},
      Volume = {8},
      Doi = {10.3389/fninf.2014.00078},
      Nest_category = {nest_technology},
      Owner = {graber},
      Timestamp = {2015.02.17},
      Url = {}

    You will find a detailed bibliography of publications on simulation technology and NEST-based computational neuroscience studies on our Publications page.

  • We teach NEST as summer schools, workshops and tutorials and provide user and developer support:
    More Activities…


How to join us

There are many ways to be a part of the NEST Community.

  • If you want to work with NEST you should definitely sign up for our NEST User and Announcement mailing lists and share your experiences with other NESTies.
  • If you want to work on NEST as a developer, stay abreast of the newest developments and contribute your own code to NEST, the NEST GitHub Repository is the place to go. Here, you will find current source code, our bug tracker, and developer discussions; plus, you can contribute your own code via pull requests.
  • As an active developer contributing code to NEST, you are welcome to join the NEST Initiative as an active member and shape the future developement of NEST. You will find more information on our Membership page.
  • If you just want to support the goals of the NEST Initiative without contributing code, you are welcome to join us as a community member, as described on our Membership page.

Welcome aboard!