NEST (NEural Simulation Tool)

By Marc-Oliver Gewaltig, Markus Diesmann. Scholarpedia, 2007

1,107 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8652da64d758b627921d2c87d44b3ce7cace6ba5

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

The Virtual Brain: a simulator of primate brain network dynamics (2013, background): “…environments simulate neural activity at the level of neurons (Brian simulator, MOOSE, PCSIM, NEURON, NEST, GENESIS) (Hines and Carnevale, 2001; Gewaltig and Diesmann, 2007; Goodman and Brette, 2008; Ray and Bhalla, 2008; Pecevski et al., 2009; Brette and Goodman, 2011), even mimicking a…”
Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks (2011, methodology): “…the growing availability of both tools suitable for large-scale neuronal network simulations (e.g., Morrison et al., 2005; Migliore et al., 2006; Gewaltig and Diesmann, 2007; Pecevski et al., 2009) and the high-performance computers on which to run them, it seems curious that there have been no…”
Spiking Neural Networks and Their Applications: A Review (2022, methodology): “…platform and Intel’s Kapoho-Bay USB chipset. https://github.com/combra-lab/ spiking-ddpg-mapless-navigation (accessed on 6 September 2021) Nest [110] STDP/RSTDP Nest focuses on the dynamics and structure of neural systems, and it is used in medical/biological applications but maps poorly to…”
Direct Training for Spiking Neural Networks: Faster, Larger, Better (2018, background): “BRIAN2 (Goodman and Brette 2009) and NEST (Gewaltig and Diesmann 2007) target the simulation of larger scale SNNs with many biological features, but not designed for the direct supervised learning for high performance discussed in this work.”
Phenomenological models of synaptic plasticity based on spike timing (2008, background): “…Malenka et al. 1988) for models of synaptic plasticity (Lisman and Zhabotinsky 2001; Shouval et al. 2002; Rubin et al. 2005; Badoual et al. 2006; Graupner and Brunel 2007; Zou and Destexhe 2007), as well as the importance of the postsynaptic voltage (Kelso et al. 1986; Artola et al. 1990;…”
The SpiNNaker Project (2014, background): “Finally, many neural languages [12], [13], [16], [17] use this abstraction natively, making it a natural choice.”
Opportunities for neuromorphic computing algorithms and applications (2022, background): “For example, NEST targets primarily computational neuroscience workloads, whereas Nengo implements computation as framed by the Neural Engineering Framework 107 .”