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NEST provides over 10 synapse models, including short-term plasticity (Tsodyks & Markram) and different variants of spike-timing dependent plasticity (STDP).

Choose from simple integrate-and-fire neurons with current or conductance based synapses, over the Izhikevich or AdEx models, to Hodgkin-Huxley models.

NEST provides over 50 neuron models many of which have been published.

To learn more about the capabilities of NEST, see the Feature summary. To get started with NEST, please see the Documentation Page for Tutorials. NEST is extensible and new models for neurons, synapses, and devices can be added.

These devices write their data either to memory or to file. To manipulate or observe the network dynamics, the experimenter can define so-called devices which represent the various instruments (for measuring and stimulation) found in an experiment. Thus, the connectivity can in general not be described by a weight or connectivity matrix but rather as an adjacency list. Any two neurons can have multiple connections with different properties. In a NEST network, different neuron and synapse models can coexist. The neural system is defined by a possibly large number of neurons and their connections. While you define your simulations in Python, the actual simulation is executed within NEST's highly optimized simulation kernel which is written in C++.Ī NEST simulation tries to follow the logic of an electrophysiological experiment that takes place inside a computer with the difference, that the neural system to be investigated must be defined by the experimenter. You can also complement PyNEST with PyNN, a simulator-independent set of Python commands to formulate and run neural simulations. With these commands, you describe and run your network simulation. PyNEST provides a set of commands to the Python interpreter which give you access to NEST's simulation kernel. You can use NEST either with the interpreted programming language Python (PyNEST) or as a stand alone application ( nest).

> NEST information brochure ( PDF) How do I use NEST? > NEST:: documented movie (short version, long version) laminar cortical networks or balanced random networks,

Models of network activity dynamics, e.g.

in the visual or auditory cortex of mammals, NEST is ideal for networks of spiking neurons of any size, for example: The development of NEST is coordinated by the NEST Initiative. 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.