Merge pull request #664 from apdavison/if_curr_delta

Add `IF_curr_delta` model

examples/synaptic_input.py

@@ -46,8 +46,18 @@
                           initial_values={"v": [-65, -55]}, label="Exponential, conductance-based")
 coba_alpha = sim.Population(2, sim.IF_cond_alpha(tau_m=10.0, i_offset=[0.0, 1.0]),
                             initial_values={"v": [-65, -55]}, label="Alpha, conductance-based")
-v_step = sim.Population(2, sim.Izhikevich(i_offset=[0.0, 0.002]),
-                        initial_values={"v": [-70, -67], "u": [-14, -13.4]}, label="Izhikevich")
+v_step_izh = sim.Population(2, sim.Izhikevich(i_offset=[0.0, 0.002]),
+                            initial_values={"v": [-70, -67], "u": [-14, -13.4]}, label="Izhikevich")
+
+all_neurons = cuba_exp + cuba_alpha + coba_exp + coba_alpha + v_step_izh
+
+try:
+    v_step_if = sim.Population(2, sim.IF_curr_delta(tau_m=10.0, i_offset=[0.0, 1.0]),
+                               initial_values={"v": [-65, -55]}, label="Voltage step")
+except NotImplementedError:
+    v_step_if = None
+else:
+    all_neurons += v_step_if
 
 # we next create a spike source, which will emit spikes at the specified times
 
@@ -56,13 +66,11 @@
 
 # now we connect the spike source to each of the neuron populations, with differing synaptic weights
 
-all_neurons = cuba_exp + cuba_alpha + coba_exp + coba_alpha + v_step
-
 connections = [sim.Projection(stimulus, population,
                               connector=sim.AllToAllConnector(),
                               synapse_type=sim.StaticSynapse(weight=w, delay=2.0),
                               receptor_type="excitatory")
-               for population, w in zip(all_neurons.populations, [1.6, 4.0, 0.03, 0.12, 1.0])]
+               for population, w in zip(all_neurons.populations, [1.6, 4.0, 0.03, 0.12, 2.0, 4.0])]
 
 # finally, we set up recording of the membrane potential
 
@@ -96,7 +104,7 @@
 if options.plot_figure:
     from pyNN.utility.plotting import Figure, Panel
     figure_filename = filename.replace("pkl", "png")
-    Figure(
+    panels = [
         Panel(cuba_exp.get_data().segments[0].filter(name='v')[0],
               ylabel="Membrane potential (mV)",
               data_labels=[cuba_exp.label], yticks=True, ylim=(-66, -50)),
@@ -106,9 +114,17 @@
               data_labels=[coba_exp.label], yticks=True, ylim=(-66, -50)),
         Panel(coba_alpha.get_data().segments[0].filter(name='v')[0],
               data_labels=[coba_alpha.label], yticks=True, ylim=(-66, -50)),
-        Panel(v_step.get_data().segments[0].filter(name='v')[0],
+        Panel(v_step_izh.get_data().segments[0].filter(name='v')[0],
               xticks=True, xlabel="Time (ms)",
-              data_labels=[v_step.label], yticks=True, ylim=(-71, -65)),
+              data_labels=[v_step_izh.label], yticks=True, ylim=(-71, -65))
+    ]
+    if v_step_if:
+        panels.insert(-1,
+            Panel(v_step_if.get_data().segments[0].filter(name='v')[0],
+                  data_labels=[v_step_if.label], yticks=True, ylim=(-66, -50)),
+        )
+    Figure(
+        *panels,
         title="Responses of standard neuron models to synaptic input",
         annotations="Simulated with %s" % options.simulator.upper()
     ).save(figure_filename)

pyNN/brian2/standardmodels/cells.py

@@ -102,6 +102,10 @@
                 tau_syn_i                                     : second
             ''')
 
+voltage_step_synapses = brian2.Equations('''
+                i_syn = 0 * amp  : amp
+            ''')
+
 leaky_iaf_translations = build_translations(
                 ('v_rest',     'v_rest',     lambda **p: p["v_rest"] * mV, lambda **p: p["v_rest"] / mV),
                 ('v_reset',    'v_reset',    lambda **p: p["v_reset"] * mV, lambda **p: p["v_reset"] / mV),
@@ -156,7 +160,6 @@
                 ('gsyn_exc', 'ge', lambda p: p * uS, lambda p: p/ uS),
                 ('gsyn_inh', 'gi', lambda p: p * uS, lambda p: p/ uS))
 current_based_variable_translations = build_translations(
-
                 ('v',         'v',         lambda p: p * mV, lambda p: p/ mV), #### change p by p["v"]
                 ('isyn_exc', 'ie',         lambda p: p * nA, lambda p: p/ nA),
                 ('isyn_inh', 'ii',         lambda p: p * nA, lambda p: p/ nA))
@@ -182,6 +185,17 @@ class IF_curr_exp(cells.IF_curr_exp):
     brian2_model = ThresholdNeuronGroup
 
 
+class IF_curr_delta(cells.IF_curr_delta):
+    __doc__ = cells.IF_curr_delta.__doc__
+    eqs = leaky_iaf + voltage_step_synapses
+    translations = deepcopy(leaky_iaf_translations)
+    state_variable_translations = build_translations(
+        ('v', 'v', lambda p: p * mV, lambda p: p/ mV),
+    )
+    post_synaptic_variables = {'excitatory': 'v', 'inhibitory': 'v'}
+    brian2_model = ThresholdNeuronGroup
+
+
 class IF_cond_alpha(cells.IF_cond_alpha):
     __doc__ = cells.IF_cond_alpha.__doc__
     eqs = leaky_iaf + conductance_based_alpha_synapses

pyNN/nest/standardmodels/cells.py

@@ -50,6 +50,29 @@ class IF_curr_exp(cells.IF_curr_exp):
     standard_receptor_type = True
 
 
+class IF_curr_delta(cells.IF_curr_delta):
+
+    __doc__ = cells.IF_curr_delta.__doc__
+
+    translations = build_translations(
+        ('v_rest',     'E_L'),
+        ('v_reset',    'V_reset'),
+        ('cm',         'C_m',      1000.0),  # C_m is in pF, cm in nF
+        ('tau_m',      'tau_m'),
+        ('tau_refrac', 't_ref'),
+        ('v_thresh',   'V_th'),
+        ('i_offset',   'I_e',      1000.0),  # I_e is in pA, i_offset in nA
+    )
+    # extra parameters in the NEST model
+    # V_min            mV      Absolute lower value for the membrane potenial
+    # refractory_input boolean If true, do not discard input during
+    #                      refractory period. Default: false
+    nest_name = {"on_grid": 'iaf_psc_delta',
+                 "off_grid": 'iaf_psc_delta_ps'}
+    standard_receptor_type = True
+    receptor_scale = 1e-3  # synaptic weight is in mV, so need to undo usual weight scaling
+
+
 class IF_cond_alpha(cells.IF_cond_alpha):
 
     __doc__ = cells.IF_cond_alpha.__doc__

pyNN/neuron/standardmodels/cells.py

@@ -7,7 +7,7 @@
 
 """
 
-from pyNN.standardmodels import cells as base_cells, build_translations
+from pyNN.standardmodels import ModelNotAvailable, cells as base_cells, build_translations
 from pyNN.neuron.cells import (StandardIF, SingleCompartmentTraub,
                                RandomSpikeSource, VectorSpikeSource,
                                RandomGammaSpikeSource,
@@ -59,6 +59,10 @@ class IF_curr_exp(base_cells.IF_curr_exp):
                         'syn_shape': 'exp'}
 
 
+class IF_curr_delta(ModelNotAvailable):
+    pass
+
+
 class IF_cond_alpha(base_cells.IF_cond_alpha):
 
     __doc__ = base_cells.IF_cond_alpha.__doc__

pyNN/standardmodels/cells.py

@@ -113,6 +113,39 @@ class IF_curr_exp(StandardCellType):
     }
 
 
+class IF_curr_delta(StandardCellType):
+    """
+    Leaky integrate and fire model with fixed threshold.
+    Synaptic inputs produce step changes in membrane potential.
+    """
+
+    default_parameters = {
+        'v_rest':   -65.0,  # Resting membrane potential in mV.
+        'cm':         1.0,  # Capacity of the membrane in nF
+        'tau_m':     20.0,  # Membrane time constant in ms.
+        'tau_refrac': 0.1,  # Duration of refractory period in ms.
+        'i_offset':   0.0,  # Offset current in nA
+        'v_reset':  -65.0,  # Reset potential after a spike in mV.
+        'v_thresh': -50.0,  # Spike threshold in mV.
+    }
+    recordable = ['spikes', 'v']
+    conductance_based = False
+    voltage_based_synapses = True
+    default_initial_values = {
+        'v': -65.0,
+    }
+    units = {
+        'v': 'mV',
+        'v_rest': 'mV',
+        'cm': 'nF',
+        'tau_m': 'ms',
+        'tau_refrac': 'ms',
+        'i_offset': 'nA',
+        'v_reset': 'mV',
+        'v_thresh': 'mV',
+    }
+
+
 class IF_cond_alpha(StandardCellType):
     """
     Leaky integrate and fire model with fixed threshold and alpha-function-