pymonntorch 0.1.4

Features

• Use torch tensors and Pytorch-like syntax to create a spiking neural network (SNN).

• Simulate an SNN on CPU or GPU.

• Define dynamics of SNN components as Behavior modules.

• Control over the order of applying different behaviors in each simulation time step.

Usage

You can use the same syntax as PymoNNto to create you network:

from pymonntorch import *

net = Network()
ng = NeuronGroup(net=net, tag="my_neuron", size=100, behavior=None)
SynapseGroup(src=ng, dst=ng, net=net, tag="recurrent_synapse")
net.initialize()
net.simulate_iterations(1000)

Similarly, you can write your own Behavior Modules with the same logic as PymoNNto; except using torch tensors instead of numpy ndarrays.

from pymonntorch import *

class BasicBehavior(Behavior):
    def initialize(self, neurons):
        super().initialize(neurons)
        neurons.voltage = neurons.vector(mode="zeros")
        self.threshold = 1.0

    def forward(self, neurons):
        firing = neurons.voltage >= self.threshold
        neurons.spike = firing.byte()
        neurons.voltage[firing] = 0.0 # reset

        neurons.voltage *= 0.9 # voltage decay
        neurons.voltage += neurons.vector(mode="uniform", density=0.1)

class InputBehavior(Behavior):
    def initialize(self, neurons):
        super().initialize(neurons)
        for synapse in neurons.afferent_synapses['GLUTAMATE']:
            synapse.W = synapse.matrix('uniform', density=0.1)
            synapse.enabled = synapse.W > 0

    def forward(self, neurons):
        for synapse in neurons.afferent_synapses['GLUTAMATE']:
            neurons.voltage += synapse.W@synapse.src.spike.float() / synapse.src.size * 10

net = Network()
ng = NeuronGroup(net=net,
                size=100,
                behavior={
                    1: BasicBehavior(),
                    2: InputBehavior(),
                    9: Recorder(['voltage']),
                    10: EventRecorder(['spike'])
                })
SynapseGroup(src=ng, dst=ng, net=net, tag='GLUTAMATE')
net.initialize()
net.simulate_iterations(1000)

import matplotlib.pyplot as plt

plt.plot(net['voltage',0][:, :10])
plt.show()

plt.plot(net['spike.t',0], net['spike.i',0], '.k')
plt.show()

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. It changes the codebase of PymoNNto to use torch rather than numpy and tensorflow numpy.

0.1.4 (2024-03-16)

• Bug fixes.

• Src and dst can be None for syanpses.

• Hierarchical structure.

0.1.3 (2023-08-16)

• BREAKING CHANGE: Network no longer accept settings. Individual setting are now argument for Network.

• Bug fixes.

0.1.2 (2023-06-14)

• tensor method for NetworkObject

0.1.1 (2023-05-26)

• Every NetworkObject can have a recorder behavior.

• Network settings accept “index” entry.

• Bug fixes and general improvement.

0.1.0 (2023-03-17)

• Repository made public.