Add Kobayashi model with PyNN

pyNN/nest/standardmodels/cells.py

@@ -397,6 +397,31 @@ class EIF_cond_exp_isfa_ista(cells.EIF_cond_exp_isfa_ista):
     standard_receptor_type = True
 
 
+class Kobayashi(cells.Kobayashi):
+
+    __doc__ = cells.Kobayashi.__doc__
+
+    translations = build_translations(
+       ('cm', 'C_m', 1000.0), # C_m is in pF, cm in nF
+       ('i_offset', 'I_e', 1000.0), # I_e is in pA, i_offset in nA
+       ('tau_m', 'tau_m'),
+       ('v_rest', 'E_L'),
+       ('tau_syn_E', 'tau_syn_ex'),
+       ('tau_syn_I', 'tau_syn_in'),
+       ('tau_refrac', 't_ref'),
+       ('tau_1', 'tau_1'),
+       ('tau_2', 'tau_2'),
+       ('alpha_1', 'alpha_1'),
+       ('alpha_2', 'alpha_2'),
+       ('v_thresh', 'omega'),
+   )
+    variable_map = {'v': 'V_m', 'v_th': 'V_th'}
+    scale_factors = {'v': 1, 'v_th': 1}
+    nest_name = {"on_grid": "mat2_psc_exp",
+                 "off_grid": "mat2_psc_exp"}
+    standard_receptor_type = True
+
+
 class Izhikevich(cells.Izhikevich):
     __doc__ = cells.Izhikevich.__doc__
 

pyNN/standardmodels/cells.py

@@ -685,6 +685,50 @@ def computed_parameters_include(self, parameter_names):
         )
 
 
+class Kobayashi(StandardCellType):
+    """
+    Leaky integrate-and-fire neuron model with exponential PSCs and adaptive threshold.
+
+    Kobayashi, R., Tsubo, Y., & Shinomoto, S. (2009). Made-to-order
+    spiking neuron model equipped with a multi-timescale adaptive
+    threshold. Frontiers in computational neuroscience, 3, 9.
+
+    """
+
+    default_parameters = {
+        'cm': 0.1,
+        'tau_m': 5.0,
+        'tau_refrac': 0.1,
+        'v_rest': -70.0,
+        'i_offset': 0.0,
+        'tau_syn_E': 1.0,
+        'tau_syn_I': 3.0,
+        'tau_1': 10.0,
+        'tau_2': 200.0,
+        'alpha_1': 37.0,
+        'alpha_2': 2.0,
+        'v_thresh': 19.0 
+    }
+    recordable = ['spikes', 'v', 'v_th']
+    units = {
+        'cm': 'nF',
+        'tau_m': 'ms',
+        'tau_refrac': 'ms',
+        'v_rest': 'mV',
+        'i_offset': 'nA',
+        'tau_syn_E': 'ms',
+        'tau_syn_I': 'ms',
+        'tau_1': 'ms',
+        'tau_2': 'ms',
+        'alpha_1': 'mV',
+        'alpha_2': 'mV',
+        'v_thresh': 'mV',
+        'v': 'mV',
+        'v_th': 'mV',
+        'v_reset': 'mV'
+    }
+
+
 class Izhikevich(StandardCellType):
     """
     Izhikevich spiking model with a quadratic non-linearity according to: