NEST is a simulation software for spiking neural network models, including large-scale neuronal networks. NEST was initially developed by Markus Diesmann and Marc-Oliver Gewaltig and is now developed and maintained by the NEST Initiative.
Modeling philosophy
A 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.
The neural system is defined by a possibly large number of neurons and their connections. In a NEST network, different neuron and synapse models can coexist. Any two neurons can have multiple connections with different properties. Thus, the connectivity can in general not be described by a weight or connectivity matrix but rather as an adjacency list.
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. These devices write their data either to memory or to file.
NEST is extensible and new models for neurons, synapses, and devices can be added.
Example
NEST raster
The following example simulates spiking activity in a sparse random network with recurrent excitation and inhibition[1]
The figure shows the spiking activity of 50 neurons as a raster plot. Time increases along the horizontal axis, neuron id increases along the vertical axis. Each dot corresponds to a spike of the respective neuron at a given time. The lower part of the figure shows a histogram with the mean firing-rate of the neurons.
importnestimportnest.raster_plotJ_ex=0.1# excitatory weightJ_in=-0.5# inhibitory weightp_rate=20000.0# external Poisson rateneuron_params={"C_m":1.0,"tau_m":20.0,"t_ref":2.0,"E_L":0.0,"V_reset":0.0,"V_m":0.0,"V_th":20.0}# Set parameters of neurons and devicesnest.SetDefaults("iaf_psc_delta",neuron_params)nest.SetDefaults("poisson_generator",{"rate":p_rate})nest.SetDefaults("spike_detector",{"withtime":True,"withgid":True})# Create neurons and devicesnodes_ex=nest.Create("iaf_psc_delta",10000)nodes_in=nest.Create("iaf_psc_delta",2500)noise=nest.Create("poisson_generator")espikes=nest.Create("spike_detector")# Configure synapse modelsnest.CopyModel("static_synapse","excitatory",{"weight":J_ex,"delay":1.5})nest.CopyModel("static_synapse","inhibitory",{"weight":J_in,"delay":1.5})# Connect the random net and instrument it with the devicesnest.Connect(nodes_ex,nodes_ex+nodes_in,{"rule":'fixed_indegree',"indegree":1000},"excitatory")nest.Connect(nodes_in,nodes_ex+nodes_in,{"rule":'fixed_indegree',"indegree":250},"inhibitory")nest.Connect(noise,nodes_ex+nodes_in,syn_spec="excitatory")nest.Connect(nodes_ex[1:51],espikes)# Simulate for 100. msnest.Simulate(100.0)# Plot resultsnest.raster_plot.from_device(espikes,hist=True)nest.raster_plot.show()
Features
Neuron models
Integrate-and-fire models with different types of synaptic currents or potentials
Support for hybrid multi-threaded and distributed simulation.
Parallelization is handled semi-automatically by NEST's simulation kernel.
Supra-linear to linear scaling for up to 10000 cores.[5]
Interoperability
Interface to the Multi Simulator Coordinator, developed by the INCF.[6]
Interface to the simulator independent simulation language PyNN.
History
NEST development was started in 1993 by Markus Diesmann and Marc-Oliver Gewaltig at the Ruhr University Bochum, Bochum, Germany and the Weizmann Institute of Science in Rehovot, Israel. At this time, the simulator was called SYNOD and simulations were defined in a stack based simulation language, called SLI.[7]
In 2001, the software changed its name from SYNOD to NEST. Until 2004, NEST was exclusively developed and used by the founding members of the NEST Initiative. The first public release appeared in summer 2004. Since then, NEST was released regularly, about once or twice per year.
Since 2007, NEST supports hybrid parallelism, using POSIX threads and MPI.[5]
In 2008, the stack-based simulation language SLI was superseded by a modern Python interface, however, the old simulation language is still used internally.[8]
At the same time, the simulator independent specification language PyNN was developed with support for NEST.[9]
In 2012, the NEST Initiative changed the license from the proprietary NEST License to GNU GPL V2 or later.
User interfaces
NEST primary user interface is PyNEST, a Python package that controls the NEST simulation kernel. PyNEST aims at easy usability and seamless interaction with Python and its libraries.
PyNN is a simulator independent language for neural simulations, which supports NEST as well as BRIAN, NEURON, as well as neuromorphic hardware.
^Henker, S.; Partzsch, J.; Schüffny, R. (2012). "Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks". Journal of Computational Neuroscience. 32 (2): 309–326. doi:10.1007/s10827-011-0353-9. PMID21837455. S2CID254601151.
