Merge pull request #28 from SpiNNakerManchester/sEMD_updates

Update sEMD example based on changes to new sEMD user's current scripts

Author: rowleya

examples/extra_models_examples/IF_curr_exp_sEMD.py

@@ -13,96 +13,95 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
+
 """
-Spiking Elementary Motion Detector (sEMD) example
+Spiking ELementary Motion Detector (sEMD) example
 See https://www.cit-ec.de/en/nbs/spiking-insect-vision for more details
 """
 
 # imports
 import spynnaker8 as p
-import datetime
-from pyNN.utility.plotting import Figure, Panel
 import matplotlib.pyplot as plt
+from pyNN.utility.plotting import Figure, Panel
 
-# parameters
-datum = datetime.datetime.now()
-
-step = 0.1
-p.setup(timestep=step)
-n_neurons = 1
-run_time = 150
-cm = 0.25
-i_offset = 0.0
-tau_m = 20.0
-tau_refrac = 1.0
-current_decay = tau_syn_E = tau_syn_I = 30
-v_reset = -85.0
-v_rest = -65.0
-v_thresh = -50.0
-
-weight = 1
-delay1 = 0.1
-delay2 = 2.0
+# variables
+weights = 1
+spike_time_facilitation = 4
+spike_time_trigger = 20
 
-cell_params_lif = {'cm': cm, 'i_offset': i_offset, 'tau_m': tau_m,
-                   'tau_refrac': tau_refrac, 'tau_syn_E': current_decay,
-                   'tau_syn_I': current_decay, 'v_reset': v_reset,
-                   'v_rest': v_rest, 'v_thresh': v_thresh}
+# set up simulation
+simulation_timestep = 1  # ms
+simulation_runtime = 100  # ms
+p.setup(timestep=simulation_timestep)
 
+# neuron parameters
+cell_params_semd = {'cm': 0.25,
+                    'i_offset': 0,  # offset current
+                    'tau_m': 10,  # membrane potential time constant
+                    'tau_refrac': 1,  # refractory period time constant
+                    'tau_syn_E': 20,  # excitatory current time constant
+                    'tau_syn_I': 20,  # inhibitory current time constant
+                    'v_reset': -85,  # reset potential
+                    'v_rest': -60,  # resting potential
+                    'v_thresh': -50  # spiking threshold
+                    }
 
 # neuron populations
-sEMD = p.Population(1, p.extra_models.IF_curr_exp_sEMD(**cell_params_lif),
-                    label="sEMD")
-spikeArray = {'spike_times': [[0]]}
-input_first = p.Population(1, p.SpikeSourceArray(**spikeArray),
-                           label="input_first")
-input_second = p.Population(1, p.SpikeSourceArray(**spikeArray),
-                            label="input_second")
+# (population size, neuron type, cell parameters, label)
+sEMD = p.Population(1, p.extra_models.IF_curr_exp_sEMD,
+                    cell_params_semd, label="sEMD")
+input_facilitation = p.Population(1, p.SpikeSourceArray,
+                                  {'spike_times': [[spike_time_facilitation]]},
+                                  label="input_facilitation")
+input_trigger = p.Population(1, p.SpikeSourceArray,
+                             {'spike_times': [[spike_time_trigger]]},
+                             label="input_trigger")
+
+sEMD.initialize(v=-60.0)
 
 # projections
-p.Projection(input_first, sEMD,
-             p.OneToOneConnector(),
-             receptor_type="excitatory",
-             synapse_type=p.StaticSynapse(weight=weight, delay=delay1))
-p.Projection(input_second, sEMD,
-             p.OneToOneConnector(),
-             receptor_type="inhibitory",
-             synapse_type=p.StaticSynapse(weight=weight, delay=delay2))
+p.Projection(input_facilitation, sEMD, p.OneToOneConnector(),
+             p.StaticSynapse(weight=weights, delay=1),
+             receptor_type='excitatory')
+p.Projection(input_trigger, sEMD, p.OneToOneConnector(),
+             p.StaticSynapse(weight=weights, delay=1),
+             receptor_type='excitatory2')
 
 # records
-sEMD.record(['v', 'gsyn_exc', 'gsyn_inh', 'spikes'])
-
-# run
-p.run(run_time)
+sEMD.record(['spikes', 'v', 'gsyn_exc', 'gsyn_inh'])
 
-# get data
-spikes = sEMD.get_data(['spikes'])  # read spikes
-v = sEMD.get_data(['v'])  # read membrane voltage
-current_exc = sEMD.get_data(['gsyn_exc'])  # read excitatory
-current_inh = sEMD.get_data(['gsyn_inh'])  # read inhibitory
+# run simulation
+p.run(simulation_runtime)
 
-print(datum)
+# receive data from neurons
+spikes = sEMD.get_data(['spikes'])
+v = sEMD.get_data(['v'])
+current_exc = sEMD.get_data(['gsyn_exc'])
+current_inh = sEMD.get_data(['gsyn_inh'])
 
 # plots
 Figure(
-    # raster plot of the presynaptic neuron spike times
+    # raster plot of the neuron spike times
     Panel(spikes.segments[0].spiketrains,
-          yticks=True, markersize=1.5, xlim=(0, run_time)),
-    # membrane potential of the postsynaptic neuron
+          yticks=True, markersize=4, xlim=(0, simulation_runtime)),
+    # membrane potential
     Panel(v.segments[0].filter(name='v')[0],
           ylabel="Membrane potential (mV)",
-          data_labels=[sEMD.label], yticks=True, xlim=(0, run_time)),
+          data_labels=[sEMD.label], yticks=True, xlim=(0, simulation_runtime)),
+    # excitatory current
     Panel(current_exc.segments[0].filter(name='gsyn_exc')[0],
           ylabel="gsyn excitatory (mV)",
-          data_labels=[sEMD.label], yticks=True, xlim=(0, run_time)),
+          data_labels=[sEMD.label], yticks=True, xlim=(0, simulation_runtime)),
+    # inhibitory current
     Panel(current_inh.segments[0].filter(name='gsyn_inh')[0],
           xlabel="Time (ms)", xticks=True,
           ylabel="gsyn inhibitory (mV)",
-          data_labels=[sEMD.label], yticks=True, xlim=(0, run_time)),
+          data_labels=[sEMD.label], yticks=True, xlim=(0, simulation_runtime)),
     title="SEMD example",
     annotations="Simulated with {}".format(p.name())
 )
 plt.show()
+# plt.savefig('results.png')
 
 # end
 p.end()