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:

Rate-coded networks

Echo-state networks: echo-state networks are the rate-coded version of reservoir computing (Jaeger, 2001).
Neural Field: a simple rate-coded model without learning using neural fields.
BCM learning rule: basic demonstration of the Intrator & Cooper BCM learning rule.
Bar learning: an implementation of the bar learning problem, illustrating synaptic plasticity in rate-coded networks.
Miconi: Reward-modulated recurrent network based on Miconi (2017).
Structural Plasticity: a dummy example demonstrating structural plasticity.

AdEx: how the AdEx neuron model (adaptive exponential) can reproduce various spiking patterns in vivo (Naud et al. 2008).
PyNN/Brian: a set of single neuron models reproducing various examples from PyNN and Brian.
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.
COBA and CUBA: 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).
STDP I and II: two simple examples 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: a simple hybrid network with both rate-coded and spiking sub-parts.
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.

Image and Convolution: shows how to use the ImagePopulation class of the image extension to clamp directly images and video streams into a rate-coded network. Also demonstrates the convolution extension.
Logging with tensorboard: a simple basal ganglia model to show how to use the tensorboard extension.
BOLD monitoring I and II: a showcase of the bold extension allowing to record BOLD signals fron a network.
ANN2SNN I and II: demonstrates the ANN-to-SNN conversion tool using the MNIST dataset for a MLP and a CNN.