nest/nest-simulator

What it solves

NEST provides a way to simulate spiking neural network models. It focuses on the dynamics, size, and structure of neural systems rather than the detailed physical shape (morphology) of individual neurons, allowing researchers to study how neural systems process information and how they behave.

How it works

NEST acts as a virtual electrophysiological experiment. The user defines the neural system to be investigated, and the simulator calculates the network activity. It is designed to be highly scalable and memory-efficient, enabling it to run on everything from a single laptop to massive supercomputers using multi-core processing.

Who it’s for

It is designed for researchers and scientists studying:

• Information processing in the visual or auditory cortex of mammals.
• Network activity dynamics, such as balanced random networks or laminar cortical networks.
• Learning and plasticity within neural systems.

Highlights

• Scalable Architecture: Works across a wide range of hardware, from personal laptops to supercomputing clusters.
• Python Interface: Offers a flexible Python API for defining and running simulations.
• Extensive Model Library: Includes a large collection of pre-built neuron and synapse models.
• Extensible: Users can add their own custom modules to expand the simulator's capabilities.