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List of notebooks#

This section provides a list of the sample models provided in the examples/ directory of the source code.

The Jupyter notebooks can be downloaded from:

https://github.com/ANNarchy/ANNarchy.github.io/tree/master/docs/example

Rate-coded networks#

  • Neural Field: a simple model using neural field recurrent networks. This is a very simple rate-coded model without learning.
  • Bar learning: an implementation of the bar learning problem, illustrating synaptic plasticity in rate-coded networks.
  • Structural Plasticity: a dummy example using structural plasticity.

Spiking networks#

Simple networks

  • Izhikevich: an implementation of the simple pulse-coupled network described in (Izhikevich, 2003). It shows how to build a simple spiking network without synaptic plasticity.
  • Gap Junctions: an example using gap junctions.
  • HodgkinHuxley: a single Hodgkin-Huxley neuron.

Complex networks

  • COBA: an implementation of the balanced network described in (Vogels and Abbott, 2005). It shows how to build a simple spiking network using integrate-and-fire neurons and sparse connectivity.
  • STP: an example of short-term plasticity based on the model of Tsodyks, Uziel and Markram (2000).

With synaptic plasticity

  • STDP: a simple example using spike-timing dependent plasticity (STDP).
  • Ramp: an example of homeostatic STDP based on the model of Carlson, Richert, Dutt and Krichmar (2013)

Hybrid networks#

  • Hybrid networks: a simple hybrid network with both rate-coded and spiking sub-parts.

Extensions#

  • Image and Webcam: shows how to use the ImagePopulation and VideoPopulation classes of the image extension to clamp directly images and video streams into a rate-coded network. Also demonstrates the convolution extension.
  • Parallel simulations: shows how to call parallel_run to run several networks in parallel.
  • Bayesian optimization: a demo showing how to use hyperopt to search for hyperparameters of a model.
  • Logging with tensorboard: a simple basal ganglia model to show how to use the tensorboard extension.
  • BOLD monitoring: a showcase of the bold extension allowing to record BOLD signals fron a network.
  • ANN2SNN: demonstrates the ANN-to-SNN conversion tool using the MNIST dataset.