PyNEST examples

Simple networks

• One neuron example

• One neuron with noise

• Two neuron example

• Balanced neuron example

• Example of an AEIF neuron with multiple synaptic rise and decay time constants

• IAF neuron example with current generator

• Initial membrane voltage

• Multi-compartment neuron example

2D Spatially-structured networks

• Pyramidal cells and interneurons

• Convergent projection and rectangular mask, from target perspective

• Convergent projection and rectangular mask, from source perspective

• Circular mask and flat probability

• Circular mask and flat probability, with edge wrap

• 4x3 grid with pyramidal cells and interneurons

• Gaussian probabilistic kernel

• 4x3 grid

• Freely placed neurons

• 4x3 grid for three populations

3D Spatially-structured networks

• A spatial network in 3D

• A spatial network in 3D with exponential connection probabilities

• A spatial network in 3D with Gaussian connection probabilities

NEST Sudoku solver

• Script controlling the simulation of a single game of Sudoku

• Generate a GIF of the network solving a Sudoku puzzle

NEST Pong game

• Application to train networks to play pong against each other

• Script to visualize a simulated Pong game.

Random balanced networks (Brunel)

• 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)

Cortical microcircuit (Potjans)

• Cortical microcircuit model

GLIF (from Allen institute)

• Conductance-based generalized leaky integrate and fire (GLIF) neuron example

• Current-based generalized leaky integrate and fire (GLIF) neuron example

Compartmental neurons

• Constructing and simulating compartmental models with different receptor types

• Constructing and simulating compartmental models with active and passive dendrites

Rate neurons

• Network of linear rate neurons

• Rate neuron decision making

GIF (from Gerstner lab)

• Example network using generalized IAF neuron with postsynaptic conductances

• Population of GIF neuron model with oscillatory behavior

• Population rate model of generalized integrate-and-fire neurons

Hodgkin-Huxley

• Example using Hodgkin-Huxley neuron

• Numerical phase-plane analysis of the Hodgkin-Huxley neuron

Brody Hopfield

• Spike synchronization through subthreshold oscillation

Brette and Gerstner

• 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)

Precise spiking

• Comparing precise and grid-based neuron models

Campbell Siegert

• Campbell & Siegert approximation example

SONATA network

• NEST SONATA network

Gap junctions

• Gap Junctions: Two neuron example

• Gap Junctions: Inhibitory network example

Structural plasticity

• Structural Plasticity example

Synapse collection

• Synapse Collection usage example

Urbanczik

• Weight adaptation according to the Urbanczik-Senn plasticity

Clopath

• Clopath Rule: Spike pairing experiment

• Clopath Rule: Bidirectional connections

Tsodyks

• Tsodyks depressing example

• Tsodyks facilitating example

• Example of the tsodyks2_synapse in NEST

Recording and analyzing networks

• Example of multimeter recording to file

• Plot weight matrices example

• IF curve example

• Pulse packet example

• Correlospinmatrix detector example

• Auto- and crosscorrelation functions for spike trains

Stimulating networks

• Sinusoidal poisson generator example

• Sinusoidal gamma generator example

• Repeated Stimulation

• Sensitivity to perturbation

• Intrinsic currents spiking

• Intrinsic currents subthreshold

Handling output

• Recording examples

• Store and restore a network simulation

• Music example

HPC benchmark

• Random balanced network HPC benchmark

Connection set algebra

• Using CSA for connection setup

• Using CSA with spatial populations