Tag Archives: NEST

NEST: The Neural Simulation Tool

Science has driven the development of the NEST simulator for the past 20 years. Originally created to simulate the propagation of synfire chains using single-processor workstations, we have pushed NEST’s capabilities continuously to address new scientific questions and computer architectures. Prominent examples include studies on spike-timing dependent plasticity in large simulations of cortical networks, the verification of mean-field models, models of Alzheimer’s and Parkinson’s disease and tinnitus. Recent developments include a significant reduction in memory requirements, as demonstrated by a record-breaking simulation of 1.86 billion neurons connected by 11.1 trillion synapses on the Japanese K supercomputer, paving the way for brain-scale simulations.

Running on everything from laptops to the world’s largest supercomputers, NEST is configured and controlled by high-level Python scripts, while harnessing the power of C++ under the hood. An extensive testsuite and systematic quality assurance ensure the reliability of NEST.

The development of NEST is driven by the demands of neuroscience and carried out in a collaborative fashion at many institutions around the world, coordinated by the non-profit member-based NEST Initiative. NEST is released under GNU Public License version 2 or later.

How NEST has been improved in HBP

Continuous dynamics

The continuous dynamics code in NEST enables simulations of rate- based model neurons in the event-based simulation scheme of the spiking simulator NEST. The technology was included and released with NEST 2.14.0.

Furthermore, additional rate-based models for the Co-Design Project “Visuo-Motor Integration” (CDP4) have been implemented and scheduled for NEST release 2.16.0.

Related publication:
Hahne et al. (2017) Front. Neuroinform. 11,34. doi:10.3389/fninf.2017.00034


NESTML is a domain-specific language that supports the specification of neuron models in a precise and concise syntax, based on the syntax of Python. Model equations can either be given as a simple string of mathematical notation or as an algorithm written in the built-in procedural language. The equations are analyzed by NESTML to compute an exact solution if possible, or use an appropriate numeric solver otherwise.

Link to this release (2018): https://github.com/nest/nestml

Related Publications:

Plotnikov et al. (2016) NESTML: a modeling language for spiking neurons.

Simulator-simulator interfaces

This technology couples the simulation software NEST and UG4 by means of the MUSIC library. NEST can only send spike trains where spiking occurs; UG4 receives those in form of events arriving at synapses (timestamps). The time course of the extracellular potential in a cube (representing a piece of tissue) is simulated based on the arriving spike data.The evolution of the membrane potential in space and time is described by the Xylouris-Wittum model.

Link to this release (2017): https://github.com/UG4

Related publications:
Vogel et al. (2014) Comput Vis Sci. 16,4. doi: 10.1007/s00791-014-0232-9Xylouris, K., Wittum, G. (2015) Front Comput Neurosci. doi: 10.3389/fncom.2015.00094

More information

NEST – A brain simulator (short movie)

NEST::documented (long movie)

NEST brochure:


Date of releaseJuly 2019
Version of softwarev2.18.0
Version of documentationv2.18.0
Software availableNEST can be run directly from a Jupyter notebook inside a Collab in the HBP Collaboratory.
Download & Information: https://www.nest-simulator.org
Latest code version: https://github.com/nest/nest-simulator
ResponsibleNEST Initiative (http://www.nest-initiative.org/)
General Contact: NEST User Mailing List (http://www.nest-simulator.org/community/)

Contact for HBP Partners:
Hans Ekkehard Plesser (NMBU/JUELICH): hans.ekkehard.plesser@nmbu.no
Dennis Terhorst (JUELICH): d.terhorst@fz-juelich.de
Requirements & dependenciesAny Unix-like operating system and basic development tools
GNU Science Library
Target system(s)All Unix-like systems
Laptop to Supercomputer; has been ported to Raspberry Pi, too