Merge pull request #475 from apdavison/stochastic-synapses

Stochastic synapses

.gitignore

@@ -21,6 +21,7 @@
 x86_64
 i386
 i686
+_build
 
 # Python files
 build

MANIFEST.in

@@ -1,4 +1,8 @@
 include pyNN/neuron/nmodl/*.mod
+include pyNN/nest/extensions/*.h
+include pyNN/nest/extensions/*.cpp
+include pyNN/nest/extensions/CMakeLists.txt
+include pyNN/nest/extensions/sli/*
 include pyNN/descriptions/templates/*/*.txt
 include test/parameters/*
 include test/system/*.py

doc/data_handling.txt

@@ -22,8 +22,8 @@ top-level data container, which contains one or more :class:`Segment`\s. Each
 :class:`Segment` is a container for data sharing a common time basis - a new
 :class:`Segment` is added every time the :func:`reset` function is called.
 
-A :class:`Segment` can contain lists of :class:`AnalogSignal`,
-:class:`AnalogSignalArray` and :class:`SpikeTrain` objects. These data objects
+A :class:`Segment` can contain lists of :class:`AnalogSignal` and :class:`SpikeTrain` objects.
+These data objects
 inherit from NumPy's array class, and so can be treated in further processing
 (analysis, visualization, etc.) in exactly the same way as NumPy arrays, but in
 addition they carry metadata about units, sampling interval, etc.

doc/neurons.txt

@@ -378,8 +378,8 @@ or written to file using :meth:`write_data()`:
 Neo see the documentation at http://packages.python.org/neo. Here, it will
 suffice to note that a :class:`Block` is the top-level container, and contains
 one or more :class:`Segments`. Each :class:`Segment` is a container for data
-that share a common time basis, and can contain lists of :class:`AnalogSignal`,
-:class:`AnalogSignalArray` and :class:`SpikeTrain` objects. These data objects
+that share a common time basis, and can contain lists of :class:`AnalogSignal`
+and :class:`SpikeTrain` objects. These data objects
 inherit from NumPy array, and so can be treated in further processing (analysis,
 visualization, etc.) in exactly the same way as plain arrays, but in addition
 they carry metadata about units, sampling interval, etc.

examples/multiquantal_synapses.py

@@ -0,0 +1,111 @@
+# encoding: utf-8
+"""
+...
+
+"""
+
+import matplotlib
+matplotlib.use('Agg')
+import numpy as np
+import neo
+from pyNN.utility import get_simulator, init_logging, normalized_filename
+
+
+# === Configure the simulator ================================================
+
+sim, options = get_simulator(("--plot-figure", "Plot the simulation results to a file.", {"action": "store_true"}),
+                             ("--debug", "Print debugging information"))
+
+if options.debug:
+    init_logging(None, debug=True)
+
+sim.setup(quit_on_end=False)
+
+
+# === Build and instrument the network =======================================
+
+spike_times = np.hstack((np.arange(10, 100, 10), np.arange(250, 350, 10)))
+spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
+
+connector = sim.AllToAllConnector()
+
+depressing = dict(U=0.8, tau_rec=100.0, tau_facil=0.0, weight=0.01, delay=0.5)
+facilitating = dict(U=0.04, tau_rec=50.0, tau_facil=200.0, weight=0.01, delay=0.5)
+
+synapse_types = {
+    'depressing, n=1':   sim.MultiQuantalSynapse(n=1, **depressing),
+    'depressing, n=5':   sim.MultiQuantalSynapse(n=5, **depressing),
+    'facilitating, n=1': sim.MultiQuantalSynapse(n=1, **facilitating),
+    'facilitating, n=5': sim.MultiQuantalSynapse(n=5, **facilitating),
+}
+
+populations = {}
+projections = {}
+for label in synapse_types:
+    populations[label] = sim.Population(1000, sim.IF_cond_exp(e_rev_I=-75, tau_syn_I=4.3), label=label)
+    populations[label].record('gsyn_inh')
+    projections[label] = sim.Projection(spike_source, populations[label], connector,
+                                        receptor_type='inhibitory',
+                                        synapse_type=synapse_types[label])
+    projections[label].initialize(a=synapse_types[label].parameter_space['n'], u=synapse_types[label].parameter_space['U'])
+
+spike_source.record('spikes')
+
+if "nest" in sim.__name__:
+    print(sim.nest.GetStatus([projections['depressing, n=5'].nest_connections[0]]))
+
+# === Run the simulation =====================================================
+
+sim.run(400.0)
+
+
+# === Save the results, optionally plot a figure =============================
+
+for label, p in populations.items():
+    filename = normalized_filename("Results", "multiquantal_synapses_%s" % label,
+                                   "pkl", options.simulator)
+    p.write_data(filename, annotations={'script_name': __file__})
+
+
+if options.plot_figure:
+    from pyNN.utility.plotting import Figure, Panel
+    #figure_filename = normalized_filename("Results", "multiquantal_synapses",
+    #                                      "png", options.simulator)
+    figure_filename = "Results/multiquantal_synapses_{}.png".format(options.simulator)
+
+    data = {}
+    for label in synapse_types:
+        data[label] = populations[label].get_data().segments[0]
+        gsyn = data[label].filter(name='gsyn_inh')[0]
+        gsyn_mean = neo.AnalogSignal(gsyn.mean(axis=1).reshape(-1, 1),
+                                     sampling_rate=gsyn.sampling_rate,
+                                     channel_index=np.array([0]))
+        gsyn_mean.name = 'gsyn_inh_mean'
+        data[label].analogsignals.append(gsyn_mean)
+    #import pdb; pdb.set_trace()
+
+    def make_panel(population, label):
+        return Panel(population.get_data().segments[0].filter(name='gsyn_inh')[0],
+                     data_labels=[label], yticks=True)
+    labels = sorted(synapse_types)
+    panels = [
+        Panel(data[label].filter(name='gsyn_inh_mean')[0],
+              data_labels=[label], yticks=True)
+        for label in labels
+    ]
+    # add ylabel to top panel in each group
+    panels[0].options.update(ylabel=u'Synaptic conductance (µS)')
+    ##panels[len(synapse_types)].options.update(ylabel='Membrane potential (mV)')
+    # add xticks and xlabel to final panel
+    panels[-1].options.update(xticks=True, xlabel="Time (ms)")
+
+    Figure(*panels,
+           title="Example of facilitating and depressing multi-quantal release synapses",
+           annotations="Simulated with %s" % options.simulator.upper()
+    ).save(figure_filename)
+    print(figure_filename)
+
+
+# === Clean up and quit ========================================================
+
+sim.end()

examples/stochastic_deterministic_comparison.py

@@ -0,0 +1,118 @@
+# encoding: utf-8
+"""
+...
+
+"""
+
+import matplotlib
+matplotlib.use('Agg')
+import numpy as np
+import neo
+from pyNN.utility import get_simulator, init_logging, normalized_filename
+
+
+# === Configure the simulator ================================================
+
+sim, options = get_simulator(("--plot-figure", "Plot the simulation results to a file.", {"action": "store_true"}),
+                             ("--debug", "Print debugging information"))
+
+if options.debug:
+    init_logging(None, debug=True)
+
+sim.setup(quit_on_end=False)
+
+
+# === Build and instrument the network =======================================
+
+spike_times = np.hstack((np.arange(10, 100, 10), np.arange(250, 350, 10)))
+spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
+
+connector = sim.AllToAllConnector()
+
+depressing = dict(U=0.8, tau_rec=100.0, tau_facil=0.0, weight=0.01, delay=0.5)
+facilitating = dict(U=0.04, tau_rec=50.0, tau_facil=200.0, weight=0.01, delay=0.5)
+
+synapse_types = {
+    'depressing, deterministic':   sim.TsodyksMarkramSynapse(**depressing),
+    'depressing, stochastic':      sim.StochasticTsodyksMarkramSynapse(**depressing),
+    'facilitating, deterministic': sim.TsodyksMarkramSynapse(**facilitating),
+    'facilitating, stochastic':    sim.StochasticTsodyksMarkramSynapse(**facilitating),
+}
+
+populations = {}
+projections = {}
+for label in synapse_types:
+    populations[label] = sim.Population(1000, sim.IF_cond_exp(e_rev_I=-75, tau_syn_I=4.3), label=label)
+    populations[label].record('gsyn_inh')
+    projections[label] = sim.Projection(spike_source, populations[label], connector,
+                                        receptor_type='inhibitory',
+                                        synapse_type=synapse_types[label])
+
+spike_source.record('spikes')
+
+
+# === Run the simulation =====================================================
+
+sim.run(400.0)
+
+
+# === Save the results, optionally plot a figure =============================
+
+for label, p in populations.items():
+    filename = normalized_filename("Results", "stochastic_comparison_%s" % label,
+                                   "pkl", options.simulator)
+    p.write_data(filename, annotations={'script_name': __file__})
+
+
+if options.plot_figure:
+    from pyNN.utility.plotting import Figure, Panel
+    #figure_filename = normalized_filename("Results", "stochastic_comparison",
+    #                                      "png", options.simulator)
+    figure_filename = "Results/stochastic_comparison_{}.png".format(options.simulator)
+
+    data = {}
+    for label in synapse_types:
+        data[label] = populations[label].get_data().segments[0]
+        if 'stochastic' in label:
+            gsyn = data[label].filter(name='gsyn_inh')[0]
+            gsyn_mean = neo.AnalogSignal(gsyn.mean(axis=1).reshape(-1, 1),
+                                         sampling_rate=gsyn.sampling_rate)
+            gsyn_mean.channel_index = neo.ChannelIndex(np.array([0]))
+            gsyn_mean.name = 'gsyn_inh_mean'
+            data[label].analogsignals.append(gsyn_mean)
+    #import pdb; pdb.set_trace()
+
+    def make_panel(population, label):
+        return Panel(population.get_data().segments[0].filter(name='gsyn_inh')[0],
+                     data_labels=[label], yticks=True)
+    panels = [
+        Panel(data['depressing, deterministic'].filter(name='gsyn_inh')[0][:, 0],
+              data_labels=['depressing, deterministic'], yticks=True,
+              ylim=[0, 0.008]),
+        Panel(data['depressing, stochastic'].filter(name='gsyn_inh_mean')[0],
+              data_labels=['depressing, stochastic mean'], yticks=True,
+              ylim=[0, 0.008]),
+        Panel(data['facilitating, deterministic'].filter(name='gsyn_inh')[0][:, 0],
+              data_labels=['facilitating, deterministic'], yticks=True,
+              ylim=[0, 0.002]),
+        Panel(data['facilitating, stochastic'].filter(name='gsyn_inh_mean')[0],
+              data_labels=['facilitating, stochastic mean'], yticks=True,
+              ylim=[0, 0.002]),
+
+    ]
+    # add ylabel to top panel in each group
+    panels[0].options.update(ylabel=u'Synaptic conductance (µS)')
+    ##panels[len(synapse_types)].options.update(ylabel='Membrane potential (mV)')
+    # add xticks and xlabel to final panel
+    panels[-1].options.update(xticks=True, xlabel="Time (ms)")
+
+    Figure(*panels,
+           title="Example of facilitating and depressing synapses in deterministic and stochastic versions",
+           annotations="Simulated with %s" % options.simulator.upper()
+    ).save(figure_filename)
+    print(figure_filename)
+
+
+# === Clean up and quit ========================================================
+
+sim.end()

examples/stochastic_synapses.py

@@ -0,0 +1,87 @@
+# encoding: utf-8
+"""
+
+docstring needed
+
+"""
+
+import matplotlib
+matplotlib.use('Agg')
+import numpy
+from pyNN.utility import get_simulator, init_logging, normalized_filename
+
+
+# === Configure the simulator ================================================
+
+sim, options = get_simulator(("--plot-figure", "Plot the simulation results to a file.", {"action": "store_true"}),
+                             ("--debug", "Print debugging information"))
+
+if options.debug:
+    init_logging(None, debug=True)
+
+sim.setup(quit_on_end=False)
+
+
+# === Build and instrument the network =======================================
+
+spike_source = sim.Population(1, sim.SpikeSourceArray(spike_times=numpy.arange(10, 100, 10)))
+
+connector = sim.AllToAllConnector()
+
+synapse_types = {
+    'static': sim.StaticSynapse(weight=0.01, delay=0.5),
+    'stochastic': sim.SimpleStochasticSynapse(p=0.5, weight=0.02, delay=0.5)
+}
+
+populations = {}
+projections = {}
+for label in 'static', 'stochastic':
+    populations[label] = sim.Population(1, sim.IF_cond_exp(), label=label)
+    populations[label].record(['v', 'gsyn_inh'])
+    projections[label] = sim.Projection(spike_source, populations[label], connector,
+                                        receptor_type='inhibitory',
+                                        synapse_type=synapse_types[label])
+
+spike_source.record('spikes')
+
+
+# === Run the simulation =====================================================
+
+sim.run(200.0)
+
+
+# === Save the results, optionally plot a figure =============================
+
+for label, p in populations.items():
+    filename = normalized_filename("Results", "stochastic_synapses_%s" % label,
+                                   "pkl", options.simulator)
+    p.write_data(filename, annotations={'script_name': __file__})
+
+
+if options.plot_figure:
+    from pyNN.utility.plotting import Figure, Panel
+    figure_filename = normalized_filename("Results", "stochastic_synapses_",
+                                          "png", options.simulator)
+    panels = []
+    for variable in ('gsyn_inh', 'v'):
+        for population in populations.values():
+            panels.append(
+                Panel(population.get_data().segments[0].filter(name=variable)[0],
+                      data_labels=[population.label], yticks=True),
+            )
+    # add ylabel to top panel in each group
+    panels[0].options.update(ylabel=u'Synaptic conductance (µS)')
+    panels[3].options.update(ylabel='Membrane potential (mV)')
+    # add xticks and xlabel to final panel
+    panels[-1].options.update(xticks=True, xlabel="Time (ms)")
+
+    Figure(*panels,
+           title="Example of simple stochastic synapses",
+           annotations="Simulated with %s" % options.simulator.upper()
+    ).save(figure_filename)
+    print(figure_filename)
+
+
+# === Clean up and quit ========================================================
+
+sim.end()