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Merge pull request #114 from SpiNNakerManchester/intro_lab
Merge in Intro lab
2 parents 4742e34 + 6364552 commit 98aed42

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‎.github/workflows/python_actions.yml

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@@ -53,12 +53,12 @@ jobs:
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uses: ./support/actions/install-matplotlib
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- name: Lint with flake8
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run: flake8 examples
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run: flake8 examples balanced_random learning sudoku synfire
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- name: Lint with pylint
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uses: ./support/actions/pylint
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with:
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package: examples
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package: examples balanced_random learning sudoku synfire
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exitcheck: 39
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validate:
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repository: SpiNNakerManchester/SupportScripts
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path: support
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- name: Run rat copyright enforcement
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if: matrix.python-version == 3.12
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uses: ./support/actions/check-copyrights
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with:
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config_file: rat_asl20.xml
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- name: Validate CITATION.cff
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if: matrix.python-version == 3.12
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uses: dieghernan/cff-validator@main

‎.gitignore

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@@ -7,4 +7,5 @@ __pycache__
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.pytest_cache
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*application_generated_data_files/
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*reports/
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.cache/
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.mypy_cache/

‎.ratexcludes

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**/SpiNNFrontEndCommon/**
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**/sPyNNaker/**
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**/sPyNNaker8/**
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**/.pylintrc
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**/*.dll
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**/*.exe
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**/*.png
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**/*linux
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**/*osx
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**/*.exe
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**/.pylintrc

‎CITATION.cff

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@@ -105,4 +105,4 @@ contact:
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name: SpiNNaker Software Team
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post-code: M13 9PL
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license: Apache-2.0
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repository: https://github.com/SpiNNakerManchester/PyNN8Examples
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repository: https://github.com/SpiNNakerManchester/PyNNExamples

‎balanced_random/__init__.py

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# Copyright (c) 2017 The University of Manchester
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.

‎balanced_random/balanced_random.py

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# Copyright (c) 2017 The University of Manchester
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import matplotlib.pyplot as pylab
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import numpy
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from pyNN.random import RandomDistribution
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from pyNN.utility.plotting import Figure, Panel
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import pyNN.spiNNaker as p
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p.setup(timestep=0.1)
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p.set_number_of_neurons_per_core(p.IF_curr_exp, 64)
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p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 64)
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n_neurons = 500
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n_exc = int(round(n_neurons * 0.8))
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n_inh = int(round(n_neurons * 0.2))
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weight_exc = 0.1
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weight_inh = -5.0 * weight_exc
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weight_input = 0.001
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pop_input = p.Population(100, p.SpikeSourcePoisson(rate=0.0),
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additional_parameters={
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"max_rate": 50.0,
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"seed": 0},
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label="Input")
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pop_exc = p.Population(n_exc, p.IF_curr_exp, label="Excitatory", seed=1)
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pop_inh = p.Population(n_inh, p.IF_curr_exp, label="Inhibitory", seed=2)
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stim_exc = p.Population(
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n_exc, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Exc",
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additional_parameters={"seed": 3})
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stim_inh = p.Population(
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n_inh, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Inh",
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additional_parameters={"seed": 4})
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delays_exc = RandomDistribution(
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"normal_clipped", mu=1.5, sigma=0.75, low=1.0, high=1.6)
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weights_exc = RandomDistribution(
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"normal_clipped", mu=weight_exc, sigma=0.1, low=0, high=numpy.inf)
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conn_exc = p.FixedProbabilityConnector(0.1)
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synapse_exc = p.StaticSynapse(weight=weights_exc, delay=delays_exc)
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delays_inh = RandomDistribution(
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"normal_clipped", mu=0.75, sigma=0.375, low=1.0, high=1.6)
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weights_inh = RandomDistribution(
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"normal_clipped", mu=weight_inh, sigma=0.1, low=-numpy.inf, high=0)
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conn_inh = p.FixedProbabilityConnector(0.1)
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synapse_inh = p.StaticSynapse(weight=weights_inh, delay=delays_inh)
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p.Projection(
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pop_exc, pop_exc, conn_exc, synapse_exc, receptor_type="excitatory")
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p.Projection(
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pop_exc, pop_inh, conn_exc, synapse_exc, receptor_type="excitatory")
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p.Projection(
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pop_inh, pop_inh, conn_inh, synapse_inh, receptor_type="inhibitory")
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p.Projection(
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pop_inh, pop_exc, conn_inh, synapse_inh, receptor_type="inhibitory")
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conn_stim = p.OneToOneConnector()
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synapse_stim = p.StaticSynapse(weight=weight_exc, delay=1.0)
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p.Projection(
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stim_exc, pop_exc, conn_stim, synapse_stim, receptor_type="excitatory")
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p.Projection(
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stim_inh, pop_inh, conn_stim, synapse_stim, receptor_type="excitatory")
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delays_input = RandomDistribution(
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"normal_clipped", mu=1.5, sigma=0.75, low=1.0, high=1.6)
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weights_input = RandomDistribution(
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"normal_clipped", mu=weight_input, sigma=0.01, low=0, high=numpy.inf)
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p.Projection(pop_input, pop_exc, p.AllToAllConnector(), p.StaticSynapse(
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weight=weights_input, delay=delays_input))
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pop_exc.initialize(
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v=RandomDistribution("uniform", low=-65.0, high=-55.0))
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pop_inh.initialize(
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v=RandomDistribution("uniform", low=-65.0, high=-55.0))
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pop_exc.record("spikes")
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p.run(1000)
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pop_input.set(rate=50.0)
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p.run(1000)
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pop_input.set(rate=10.0)
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p.run(1000)
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pop_input.set(rate=20.0)
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p.run(1000)
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data = pop_exc.get_data("spikes")
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end_time = p.get_current_time()
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p.end()
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Figure(
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# raster plot of the presynaptic neuron spike times
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Panel(data.segments[0].spiketrains,
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yticks=True, markersize=2.0, xlim=(0, end_time)),
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title="Balanced Random Network",
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annotations="Simulated with {}".format(p.name())
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)
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pylab.show()

‎balanced_random/split/balanced_random_split.py

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import pylab
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import numpy
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from pyNN.random import RandomDistribution
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from pyNN.utility.plotting import Figure, Panel
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import pyNN.spiNNaker as p
6+
from spynnaker.pyNN.extra_algorithms.splitter_components import (
7+
SplitterPoissonDelegate, SplitterAbstractPopulationVertexNeuronsSynapses)
8+
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p.setup(timestep=0.1, time_scale_factor=1)
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p.set_number_of_neurons_per_core(p.IF_curr_exp, 64)
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p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 64)
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n_neurons = 500
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n_exc = int(round(n_neurons * 0.8))
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n_inh = int(round(n_neurons * 0.2))
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weight_exc = 0.1
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weight_inh = -5.0 * weight_exc
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weight_input = 0.001
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19+
pop_input_splitter = SplitterPoissonDelegate()
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pop_input = p.Population(100, p.SpikeSourcePoisson(rate=0.0),
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additional_parameters={
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"max_rate": 50.0,
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"seed": 0,
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"splitter": pop_input_splitter},
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label="Input")
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27+
pop_exc_splitter = \
28+
SplitterAbstractPopulationVertexNeuronsSynapses(1, 128, False)
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pop_exc = p.Population(n_exc, p.IF_curr_exp, label="Excitatory",
30+
additional_parameters={"splitter": pop_exc_splitter,
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"seed": 1})
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pop_inh_splitter = \
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SplitterAbstractPopulationVertexNeuronsSynapses(1, 128, False)
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pop_inh = p.Population(n_inh, p.IF_curr_exp, label="Inhibitory",
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additional_parameters={"splitter": pop_inh_splitter,
36+
"seed": 2})
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stim_exc_splitter = SplitterPoissonDelegate()
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stim_exc = p.Population(
39+
n_exc, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Exc",
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additional_parameters={"seed": 3, "splitter": stim_exc_splitter})
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stim_inh_splitter = SplitterPoissonDelegate()
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stim_inh = p.Population(
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n_inh, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Inh",
44+
additional_parameters={"seed": 4, "splitter": stim_inh_splitter})
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delays_exc = RandomDistribution(
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"normal_clipped", mu=1.5, sigma=0.75, low=1.0, high=1.6)
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weights_exc = RandomDistribution(
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"normal_clipped", mu=weight_exc, sigma=0.1, low=0, high=numpy.inf)
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conn_exc = p.FixedProbabilityConnector(0.1)
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synapse_exc = p.StaticSynapse(weight=weights_exc, delay=delays_exc)
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delays_inh = RandomDistribution(
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"normal_clipped", mu=0.75, sigma=0.375, low=1.0, high=1.6)
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weights_inh = RandomDistribution(
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"normal_clipped", mu=weight_inh, sigma=0.1, low=-numpy.inf, high=0)
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conn_inh = p.FixedProbabilityConnector(0.1)
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synapse_inh = p.StaticSynapse(weight=weights_inh, delay=delays_inh)
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p.Projection(
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pop_exc, pop_exc, conn_exc, synapse_exc, receptor_type="excitatory",
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label="exc_exc")
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p.Projection(
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pop_exc, pop_inh, conn_exc, synapse_exc, receptor_type="excitatory",
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label="exc_inh")
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p.Projection(
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pop_inh, pop_inh, conn_inh, synapse_inh, receptor_type="inhibitory",
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label="inh_inh")
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p.Projection(
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pop_inh, pop_exc, conn_inh, synapse_inh, receptor_type="inhibitory",
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label="inh_exc")
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conn_stim = p.OneToOneConnector()
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synapse_stim = p.StaticSynapse(weight=weight_exc, delay=1.0)
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p.Projection(
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stim_exc, pop_exc, conn_stim, synapse_stim, receptor_type="excitatory",
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label="stim_exc_exc")
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p.Projection(
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stim_inh, pop_inh, conn_stim, synapse_stim, receptor_type="excitatory",
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label="stim_inh_inh")
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delays_input = RandomDistribution(
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"normal_clipped", mu=1.5, sigma=0.75, low=1.0, high=1.6)
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weights_input = RandomDistribution(
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"normal_clipped", mu=weight_input, sigma=0.01, low=0, high=numpy.inf)
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p.Projection(pop_input, pop_exc, p.AllToAllConnector(), p.StaticSynapse(
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weight=weights_input, delay=delays_input),
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label="input_exc")
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pop_exc.initialize(
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v=RandomDistribution("uniform", low=-65.0, high=-55.0))
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pop_inh.initialize(
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v=RandomDistribution("uniform", low=-65.0, high=-55.0))
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pop_exc.record("spikes")
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p.run(1000)
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pop_input.set(rate=50.0)
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p.run(1000)
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pop_input.set(rate=10.0)
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p.run(1000)
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pop_input.set(rate=20.0)
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p.run(1000)
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data = pop_exc.get_data("spikes")
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end_time = p.get_current_time()
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p.end()
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Figure(
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# raster plot of the presynaptic neuron spike times
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Panel(data.segments[0].spiketrains,
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yticks=True, markersize=2.0, xlim=(0, end_time)),
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title="Balanced Random Network",
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annotations="Simulated with {}".format(p.name())
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)
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pylab.show()

‎balanced_random/split/spynnaker.cfg

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[Mode]
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violate_1ms_wall_clock_restriction = True

‎integration_tests/script_builder.py

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too_long = {}
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too_long["stdp_triplet.py"] = "10 minutes"
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self.create_test_scripts(["examples"], too_long, exceptions)
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self.create_test_scripts(
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["examples","balanced_random", "learning", "synfire"],
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too_long, exceptions)
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if __name__ == '__main__':

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