Skip to content

Latest commit

 

History

History
67 lines (50 loc) · 2.85 KB

building_networks.rst

File metadata and controls

67 lines (50 loc) · 2.85 KB
Raw
.. py:currentmodule:: ml_genn

Building networks

One of the key aims of mlGeNN is to make it simple to define Spiking Neural Networks (SNNs) with arbitrary topologies. Networks consist of homogeneous groups of neurons described by :class:`~population.Population` objects connected together with :class:`~connection.Connection` objects. All populations and connections are owned by a :class:`Network` which acts as a context manager so a network with two populations of neurons could simply be created like:

from ml_genn import Connection, Population, Network

network = Network()
with network:
    a = Population("poisson_input", 100)
    b = Population("integrate_fire", 100, readout="spike_count")

    Connection(a, b, Dense(1.0))

For simplicity, in this example, built-in neuron models with default parameters are specified using strings. However, if you wish to override some of the default model parameters, use a model that does not have default parameters or use a model not built into mlGeNN, you can also specify a neuron model using a :class:`~neurons.neuron.Neuron` class instance. For example, if we wished for the Poisson population to emit positive and negative spikes for positive and negative input values and for the integrate-and-fire neuron to have a higher firing threshold we could instantiate :class:`~neurons.poisson_input.PoissonInput` and :class:`~neurons.integrate_fire.IntegrateFire` objects ourselves like:

from ml_genn import Connection, Population, Network
from ml_genn.neurons import PoissonInput, IntegrateFire

network = Network()
with network:
    a = Population(PoissonInput(signed_spikes=True), 100)
    b = Population(IntegrateFire(v_thresh=2.0), 100, readout="spike_count")

    Connection(a, b, Dense(1.0))

By default, :class:`~connection.Connection` objects use a 'delta' synapse model where the accumulated weight of incoming spikes is directly injected into neurons. However, if you wish to use a somewhat more realistic model where inputs are shaped to mimic the dynamics of real ion channels, this can be replaced. This same general principle is also used for specifying and configuring many other aspects of mlGeNN networks, including :mod:`ml_genn.losses`, :mod:`ml_genn.metrics` and :mod:`ml_genn.readouts`.

Sequential networks

While the flexibility to create networks with any topology is very useful, feed-forward networks are very common so mlGeNN provides a shorthand syntax for specifying them more tersely:

from ml_genn import InputLayer, Layer, SequentialNetwork

network = SequentialNetwork()
with network:
    a = InputLayer("poisson_input", 100)
    b = Layer(Dense(1.0), "integrate_fire", 100, readout="spike_count")