Table of ContentsΒΆ
User Documentation
- Download NEST
- Installation Instructions
- Getting Started
- Tutorials
- Part 1: Neurons and simple neural networks
- Part 2: Populations of neurons
- Part 3: Connecting networks with synapses
- Part 4: Topologically structured networks
- Introduction to the MUSIC Interface
- Connect two NEST simulations using MUSIC
- MUSIC Connections in C++ and Python
- The pymusic interface
- Practical Tips
- Video Tutorial Series
- Model Directory
- NEST Example Networks
- One neuron example
- One neuron with noise
- Two neuron example
- Balanced neuron example
- IAF Neuron example
- Repeated Stimulation
- Multimeter to file example
- 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
- MUSIC example
- Topology
- Guides
- Getting Help
- Reference Material
Getting Involved