Run rate networks as event-based models in GeNN.

Experiments

Experiments using the microcircuit architecture can be found in mc.experiments.mc. Refer to the docstring of the respective run.py files for details on running the models.

GeNN Model Definitions

mc.genn_models contains subfolders with genn model definitions of neurons and synapses. See drop_in_synapsesas a starting point when defining new neuron or synapse models.

Network Architectures

mc.network_architectures contains definitions of synapses, layers and networks that are all derived from base classes found in mc.network_base.

Building and Running a Network Model

The NetworkBase class in mc.network_base.network is an abstract base class, i.e. you need you need to define a child network class that inherits from NetworkBase when defining a network model. In particular, NetworkBase has an abstract method setup that has to be defined in your child class, and this is where GeNN model definitions and network the architecture come together into the final model.

To build your network in setup, you should use the LayerBase and SynapseBase classes (or custom classes derived from them) found in mc.network_base.layer and mc.network_base.synapse, respectively. This is not strictly necessary, but it automates the extra steps involved in setting up neuron populations and synapse populations for training and testing, in particular adding custom updates to the model for weight updates.

An example for building and running a model is given in mc.min_example.py. For simplicity, the construction of neuron and synapse model definitions was done in the main script, but, as described above, it is generally recommended to do so in an extra directory in mc.genn_models.