NEST Example Networks

• One neuron example
• One neuron with noise
• Two neuron example
• Balanced neuron example
• IAF Neuron example
• Repeated Stimulation
• Example of multimeter recording to file
• Sensitivity to perturbation
• Plot weight matrices example
• IF curve example
• Pulse packet example
• Correlospinmatrix detector example
• Auto- and crosscorrelation functions for spike trains
• Campbell & Siegert approximation example
• Spike synchronization through subthreshold oscillation
• Example using Hodgkin-Huxley neuron
• Numerical phase-plane analysis of the Hodgkin-Huxley neuron
• Structural Plasticity example
• Gap Junctions: Two neuron example
• Gap Junctions: Inhibitory network example
• Population of GIF neuron model with oscillatory behavior
• Population rate model of generalized integrate-and-fire neurons
• Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 2C)
• Testing the adapting exponential integrate and fire model in NEST (Brette and Gerstner Fig 3D)
• Multi-compartment neuron example
• Tsodyks depressing example
• Tsodyks facilitating example
• Example of the tsodyks2_synapse in NEST
• Example for the quantal_stp_synapse
• Intrinsic currents spiking
• Intrinsic currents subthreshold
• Network of linear rate neurons
• Rate neuron decision making
• Comparing precise and grid-based neuron models
• Sinusoidal poisson generator example
• Sinusoidal gamma generator example
• Clopath Rule: Spike pairing experiment
• Clopath Rule: Bidirectional connections
• Random balanced network (alpha synapses) connected with NumPy
• Random balanced network (alpha synapses) connected with NEST
• Random balanced network (delta synapses)
• Mean-field theory for random balanced network
• Random balanced network (exp synapses, multiple time constants)
• Use evolution strategies to find parameters for a random balanced network (alpha synapses)
• Using CSA for connection setup
• Using CSA with Topology layers
• Random balanced network HPC benchmark

Microcircuit Example

Hendrik Rothe, Hannah Bos, Sacha van Albada

Description

This is a PyNEST implementation of the microcircuit model by Potjans and

• This example contains several files:

  • helpers.py Helper functions for the simulation and evaluation of the microcircuit.

  • network.py Gathers all parameters and connects the different nodes with each other.

  • network_params.py Contains the parameters for the network.

  • sim_params.py Contains the simulation parameters.

  • stimulus_params.py Contains the parameters for the stimuli.

  • example.py Use this script to try out the microcircuit.

How to use the Microcircuit model example:

To run the microcircuit on a local machine, we have to first check that the variables N_scaling and K_scaling in network_params.py are set to 0.1. N_scaling adjusts the number of neurons and K_scaling adjusts the number of connections to be simulated. The full network can be run by adjusting these values to 1. If this is done, the option to print the time progress should be set to False in the file sim_params.py. For running, use python example.py. The output will be saved in the directory data.

The code can be parallelized using OpenMP and MPI, if NEST has been built with these applications (Parallel computing with NEST). The number of threads (per MPI process) can be chosen by adjusting local_num_threads in sim_params.py. The number of MPI processes can be set by choosing a reasonable value for num_mpi_prc and then running the script with the following command.

mpirun -n num_mpi_prc python example.py

The default version of the simulation uses Poissonian input, which is defined in the file network_params.py to excite neuronal populations of the microcircuit. If no Poissonian input is provided, DC input is calculated, which should approximately compensate the Poissonian input. It is also possible to add thalamic stimulation to the microcircuit or drive it with constant DC input. This can be defined in the file stimulus_params.py.

• Pynest microcircuit example
• Pynest microcircuit network
  • Authors
• Pynest microcircuit helpers
  • Authors
• Pynest microcircuit parameters
• Microcircuit stimulus parameters
• Microcircuit simulation parameters

MUSIC example

Requirements

• MUSIC 1.1.15 or higher

• NEST 2.14.0 or higher compiled with MPI and MUSIC

• NumPy

Instructions

This example runs 2 NEST instances and one receiver instance. Neurons on the NEST instances are observed by the music_cont_out_proxy and their values are forwarded through MUSIC to the receiver.

mpiexec -np 3 music test.music

• Music example
• Music example receiver script