Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

# PyNEST examples¶

- One neuron example
- One neuron with noise
- Two neuron example
- Balanced neuron example
- Spike synchronization through subthreshold oscillation
- Campbell & Siegert approximation example
- Example using Hodgkin-Huxley neuron
- Numerical phase-plane analysis of the Hodgkin-Huxley neuron
- 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
- Comparing precise and grid-based neuron models

- Structural Plasticity example
- Gap Junctions: Two neuron example
- Gap Junctions: Inhibitory network example
- Example of the tsodyks2_synapse in NEST
- Example for the quantal_stp_synapse
- Clopath Rule: Spike pairing experiment
- Clopath Rule: Bidirectional connections
- Tsodyks depressing example
- Tsodyks facilitating example

- 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)

- 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
- Sinusoidal poisson generator example
- Sinusoidal gamma generator example
- Auto- and crosscorrelation functions for spike trains
- Intrinsic currents spiking
- Intrinsic currents subthreshold

- NEST spatial example: A case-based tutorial
- Create two populations of pyramidal cells and two populations of interneurons
- Connect two populations with convergent projection and rectangular mask, visualize connection from target perspective
- Connect two populations with convergent projection and rectangular mask, visualize connections from source perspective
- Connect with circular mask, flat probability using 2 populations of iaf_psc_alpha neurons
- Create a 4x3 grid with one pyramidal cell and one interneuron at each position
- Create two populations on a 30x30 grid and connect them using a Gaussian probabilistic kernel
- Create a population of iaf_psc_alpha neurons on a 4x3 grid
- Create 12 freely placed iaf_psc_alpha neurons
- Create three populations of iaf_psc_alpha neurons on a 4x3 grid, each with different center
- A spatial network in 3D
- A spatial network in 3D with exponential connection probabilities
- A spatial network in 3D with Gaussian connection probabilities