NEST

NEST (neural simulation tool): The Neural Simulation Tool NEST is a computer program for simulating large heterogeneous networks of point neurons or neurons with a small number of compartments. NEST is best suited for models that focus on the dynamics, size, and structure of neural systems rather than on the detailed morphological and biophysical properties of individual neurons. Examples are: Models of sensory processing e.g. in the visual or auditory cortex of mammals. Models of network activity dynamics, e.g. in laminar cortical networks or random balanced networks. Models of spike-synchronization in feed-forward networks such as Synfire Chains. Learning and plasticity in models of sensory processing. NEST is developed by the NEST Initiative and is available free of charge under the GNU General Public License.


References in zbMATH (referenced in 32 articles )

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  1. Dazza, Mallory; Métens, Stephane; Monceau, Pascal; Bottani, Samuel: A novel methodology to describe neuronal networks activity reveals spatiotemporal recruitment dynamics of synchronous bursting states (2021)
  2. Romaro, Cecilia; Najman, Fernando Araujo; Lytton, William W.; Roque, Antonio C.; Dura-Bernal, Salvador: NetPyNE implementation and scaling of the Potjans-Diesmann cortical microcircuit model (2021)
  3. Schmuker, Michael; Kupper, Rüdiger; Aertsen, Ad; Wachtler, Thomas; Gewaltig, Marc-Oliver: Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code (2021)
  4. Yin, Yonghua: Random neural network methods and deep learning (2021)
  5. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  6. Huyck, Christian Robert; Vergani, Alberto Arturo: Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons (2020)
  7. Andalibi, Vafa; Hokkanen, Henri; Vanni, Simo: Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework (2019)
  8. Chrysanthidis, Nikolaos; Fiebig, Florian; Lansner, Anders: Introducing double bouquet cells into a modular cortical associative memory model (2019)
  9. Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute T.; Plesser, Hans E.: Firing-rate models for neurons with a broad repertoire of spiking behaviors (2018)
  10. Mostafa, Hesham; Cauwenberghs, Gert: A learning framework for winner-take-all networks with stochastic synapses (2018)
  11. Lytton, William W.; Seidenstein, Alexandra H.; Dura-Bernal, Salvador; McDougal, Robert A.; Schürmann, Felix; Hines, Michael L.: Simulation neurotechnologies for advancing brain research: parallelizing large networks in NEURON (2016)
  12. Sun, Haoqi; Sourina, Olga; Huang, Guang-Bin: Learning polychronous neuronal groups using joint weight-delay spike-timing-dependent plasticity (2016)
  13. Zaytsev, Yury V.; Morrison, Abigail; Deger, Moritz: Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity (2015)
  14. Gürcan, Önder: Effective connectivity at synaptic level in humans: a review and future prospects (2014)
  15. Le Mouel, Charlotte; Harris, Kenneth D.; Yger, Pierre: Supervised learning with decision margins in pools of spiking neurons (2014)
  16. Yim, Man Yi; Kumar, Arvind; Aertsen, Ad; Rotter, Stefan: Impact of correlated inputs to neurons: modeling observations from \textitinvivo intracellular recordings (2014)
  17. Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Casti, Alex; Einevoll, Gaute T.; Plesser, Hans E.: Firing-rate models capture essential response dynamics of LGN relay cells (2013)
  18. Sarvestani, Iman Kamali; Kozlov, Alexander; Harischandra, Nalin; Grillner, Sten; Ekeberg, Örjan: A computational model of visually guided locomotion in lamprey (2013) ioport
  19. Wybo, Willem A. M.; Stiefel, Klaus M.; Torben-Nielsen, Benjamin: The Green’s function formalism as a bridge between single- and multi-compartmental modeling (2013)
  20. Bray, Laurence C. Jayet; Anumandla, Sridhar R.; Thibeault, Corey M.; Hoang, Roger V.; Goodman, Philip H.; Dascalu, Sergiu M.; Bryant, Bobby D.; Jr., Frederick C. Harris: Real-time human-robot interaction underlying neurorobotic trust and intent recognition (2012) ioport

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