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
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.
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
Plotnikov et al. (2016) NESTML: a modeling language for spiking neurons.
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
NEST – A brain simulator (short movie)
NEST::documented (long movie)
|Date of release||July 2019|
|Version of software||v2.18.0|
|Version of documentation||v2.18.0|
|Software available||NEST 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
|Responsible||NEST 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): firstname.lastname@example.org
Dennis Terhorst (JUELICH): email@example.com
|Requirements & dependencies||Any 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