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LogoPyNN 0.12.3 documentation
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LogoPyNN 0.12.3 documentation
  • Introduction
  • Installation
  • Building networks
  • Injecting current
  • Recording spikes and state variables
  • Data handling
  • Simulation control
  • Model parameters and initial values
  • Random numbers
  • Multicompartmental modelling
  • Backends
  • Running parallel simulations
  • Units
  • Importing from and exporting to other formats
  • Examples
  • Publications about, relating to or using PyNN
  • Contributors, licence and funding
  • Release notes
  • Developers’ guide
    • Bug reports and feature requests
    • Contributing to PyNN
    • Governance
  • API reference
    • Populations, Views and Assemblies
    • Connectors
    • Projections
    • Neuron models
    • Synapse models
    • Current sources
    • Simulation control
    • Random numbers
    • Parameter handling
    • Spatial structure
    • Utility classes and functions
  • Standard models
PyNN 0.12.3 documentation
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Examples

Examples¶

  • A selection of Izhikevich neurons
  • Injecting time-varying current into a cell
  • A demonstration of the responses of different standard neuron models to current injection
  • An example to illustrate random number handling in PyNN
  • Illustration of the different standard random distributions and different random number generators
  • A very simple example of using STDP
  • Small network created with the Population and Projection classes
  • A demonstration of the responses of different standard neuron models to synaptic input
  • Example of depressing and facilitating synapses
  • A demonstration of the use of callbacks to vary the rate of a SpikeSourcePoisson
  • Example of simple stochastic synapses
  • Example of facilitating and depressing synapses in deterministic and stochastic versions
  • Balanced network of excitatory and inhibitory neurons

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