NEST
A simulator for spiking neural network models of any size.
- Widely used in computational neuroscience, neurorobotics and machine learning.
- Perform simulations through PyNEST, PyNN and NEST Desktop graphical user interface.
- Extend NEST without programming experience with the NESTML modelling language.
- Runs on laptops and scales to future exascale computers, thanks to NEST parallel simulation technology advances.
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems, rather than on the exact morphology of individual neurons. It is ideal for networks of any size, including models of information processing (e.g. in the visual or auditory cortex of mammals), models of network activity dynamics (e.g. laminar cortical networks or balanced random networks) and models of learning and plasticity. NEST is openly available for download.
Perform spiking neural network simulations with NEST
NEST is a command line tool for simulating neural networks. Such simulations try to follow the logic of an electrophysiological experiment. The main difference is that it takes place inside a computer, rather than the physical world. To simulate neural networks, NEST can be used interactively from the Python prompt. NEST allows users to create neuron models and devices, connect neurons to devices, specify synapse properties and simulate the network for a given number of milliseconds.
User story
Microcircuit model
NEST is built for simulations of large networks composed of interconnected simple neuron models. An example of such a model that can be simulated with NEST is the cortical microcircuit. Originally described by Potjans and Diesmann in 2014, the microcircuit model is a building block for larger networks and is of neuroscientific relevance because of the realistic proximity of neurons and synapses. It is a data-driven, full-scale spiking network model of 1 mm² of cortex which relates structure and activity. The model comprises four cortical layers, each containing an excitatory and an inhibitory population, with some 77,000 neurons in total.
A reference implementation is written in PyNEST, the easy Python interface to NEST with commands like Create(), Connect() and Simulate(). As a reference, it has been widely cited and the implementation has been reused in a considerable number of peer-reviewed papers since its original publication.
Background
Learn about the philosophy behind NEST
One of the long-term core developers of NEST discusses the approach to neuronal network simulation and scientific tool development that has driven NEST development throughout its 25-year history.
Community
Get involved in the NEST community
Since its first release in 1994, NEST has gathered a large, experienced user and developer community.
The community ensures systematic code review and continuous integration testing to maintain high code quality standards. Beyond daily collaboration via GitHub, users and developers can interact via the NEST User Mailing List, NEST Hackathons, fortnightly Open NEST Developer Video Conferences, and the annual NEST Conference. At the conference, everyone is welcome to join talks and discussions, share success stories, exchange advice and learn about current developments in and beyond NEST spiking network simulation and its applications.
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