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neuromodulation with vogels_2011 additive
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‎examples/extra_models_examples/stdp_neuromodulated_vogels.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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"""
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Simple test for neuromodulated-STDP
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We take 10 populations of 5 stimuli neurons and connect to each
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10 post-synaptic populations of 5 neurons. The spiking of stimuli causes some
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spikes in post-synaptic neurons initially.
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We then inject reward signals from dopaminergic neurons
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periodically to reinforce synapses that are active. This
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is followed by increased weights of some synapses and thus
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increased response to the stimuli.
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We then proceed to inject punishment signals from dopaminergic
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neurons which causes an inverse effect to reduce response of
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post-synaptic neurons to the same stimuli.
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"""
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import pyNN.spiNNaker as sim
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import pylab
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timestep = 1.0
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stim_rate = 50
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duration = 12000
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plastic_weights = 1.5
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n_neurons = 5
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n_pops = 10
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# Times of rewards and punishments
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rewards = [x for x in range(2000, 2010)] + \
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[x for x in range(3000, 3020)] + \
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[x for x in range(4000, 4100)]
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punishments = [x for x in range(6000, 6010)] + \
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[x for x in range(7000, 7020)] + \
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[x for x in range(8000, 8100)]
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cell_params = {'cm': 0.25,
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'i_offset': 0.0,
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'tau_m': 20.0,
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'tau_refrac': 2.0,
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'tau_syn_E': 1.0,
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'tau_syn_I': 1.0,
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'v_reset': -70.0,
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'v_rest': -65.0,
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'v_thresh': -50.0
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}
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sim.setup(timestep=timestep)
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# Create a population of dopaminergic neurons for reward and punishment
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reward_pop = sim.Population(n_neurons, sim.SpikeSourceArray,
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{'spike_times': rewards}, label='reward')
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punishment_pop = sim.Population(n_neurons, sim.SpikeSourceArray,
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{'spike_times': punishments},
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label='punishment')
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pre_pops = []
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stimulation = []
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post_pops = []
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reward_projections = []
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punishment_projections = []
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plastic_projections = []
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stim_projections = []
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# Create synapse dynamics with neuromodulated STDP.
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synapse_dynamics = sim.STDPMechanism(
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timing_dependence=sim.extra_models.Vogels2011Rule(
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alpha=0.12, tau=20.0, A_plus=0.05),
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weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=20),
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weight=plastic_weights)
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for i in range(n_pops):
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stimulation.append(sim.Population(n_neurons, sim.SpikeSourcePoisson,
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{'rate': stim_rate, 'duration': duration}, label="pre"))
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post_pops.append(sim.Population(
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n_neurons, sim.IF_curr_exp, cell_params, label='post'))
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plastic_projections.append(
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sim.Projection(stimulation[i], post_pops[i],
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sim.OneToOneConnector(),
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synapse_type=synapse_dynamics,
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receptor_type='excitatory',
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label='Pre-post projection'))
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post_pops[i].record('spikes')
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reward_projections.append(sim.Projection(
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reward_pop, post_pops[i], sim.OneToOneConnector(),
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synapse_type=sim.extra_models.Neuromodulation(
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weight=0.05, tau_c=100.0, tau_d=5.0, w_max=20.0),
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receptor_type='reward', label='reward synapses'))
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punishment_projections.append(sim.Projection(
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punishment_pop, post_pops[i], sim.OneToOneConnector(),
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synapse_type=sim.extra_models.Neuromodulation(
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weight=0.05, tau_c=100.0, tau_d=5.0, w_max=20.0),
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receptor_type='punishment', label='punishment synapses'))
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sim.run(duration)
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# Graphical diagnostics
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def plot_spikes(_spikes, _title, _n_neurons):
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"""
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Plots the spikes if there are any.
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:param list([int, quantity]) _spikes:
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:param str _title:
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:param int _n_neurons:
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"""
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if _spikes is not None:
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pylab.figure(figsize=(15, 5))
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pylab.xlim((0, duration))
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pylab.ylim((0, (10 * _n_neurons) + 1))
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pylab.plot([i[1] for i in _spikes], [i[0] for i in _spikes], ".")
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pylab.xlabel('Time/ms')
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pylab.ylabel('spikes')
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pylab.title(_title)
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else:
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print("No spikes received")
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post_spikes = []
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weights = []
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for i in range(n_pops):
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weights.append(plastic_projections[i].get('weight', 'list'))
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spikes = post_pops[i].get_data('spikes').segments[0].spiketrains
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for j in range(n_neurons):
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for x in spikes[j]:
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post_spikes.append([(i*n_neurons)+j+1, x])
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plot_spikes(post_spikes, "post-synaptic neuron activity", n_neurons)
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pylab.plot(rewards, [0.5 for x in rewards], 'g^')
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pylab.plot(punishments, [0.5 for x in punishments], 'r^')
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pylab.show()
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print("Weights(Initial {plastic_weights})")
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for x in weights:
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print(x)
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sim.end()

‎integration_tests/test_scripts.py‎

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@@ -51,12 +51,6 @@ def test_examples_split_examples_pynnBrunnelSplit(self):
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def test_examples_split_examples_va_benchmark_split(self):
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self.check_script("examples/split_examples/va_benchmark_split.py")
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def test_examples_balanced_random_balanced_random(self):
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self.check_script("examples/balanced_random/balanced_random.py")
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def test_examples_balanced_random_split_balanced_random_split(self):
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self.check_script("examples/balanced_random/split/balanced_random_split.py")
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def test_examples_synfire_if_curr_exp_random(self):
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self.check_script("examples/synfire_if_curr_exp_random.py")
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def test_examples_extra_models_examples_vogel_2011_vogels_2011(self):
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self.check_script("examples/extra_models_examples/vogel_2011/vogels_2011.py")
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def test_examples_extra_models_examples_stdp_neuromodulated_vogels(self):
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self.check_script("examples/extra_models_examples/stdp_neuromodulated_vogels.py")
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def test_examples_extra_models_examples_stdp_associative_memory(self):
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self.check_script("examples/extra_models_examples/stdp_associative_memory.py")
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def test_examples_extra_models_examples_IF_cond_exp_stoc(self):
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self.check_script("examples/extra_models_examples/IF_cond_exp_stoc.py")
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def test_examples_wta_example(self):
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self.check_script("examples/wta_example.py")
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def test_balanced_random_balanced_random(self):
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self.check_script("balanced_random/balanced_random.py")
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