Add an alternative version of aeif_cond_alpha neuron model

models/neurons/aeif_cond_alpha_alt_neuron.nestml

@@ -1,5 +1,5 @@
 """
-aeif_psc_alpha - Conductance based exponential integrate-and-fire neuron model
+aeif_cond_alpha - Conductance based exponential integrate-and-fire neuron model
 ###############################################################################
 
 Description
@@ -39,13 +39,12 @@ See also
 
 iaf_psc_alpha, aeif_psc_exp
 """
-model aeif_psc_alpha_neuron:
+model aeif_cond_alpha_alt_neuron:
 
     state:
         V_m mV = E_L       # Membrane potential
         w pA = 0 pA        # Spike-adaptation current
         refr_t ms = 0 ms    # Refractory period timer
-        is_refractory integer = 0
         g_exc nS = 0 nS      # AHP conductance
         g_exc' nS/ms = 0 nS/ms   # AHP conductance
         g_inh nS = 0 nS      # AHP conductance
@@ -60,12 +59,14 @@ model aeif_psc_alpha_neuron:
         # Add inlines to simplify the equation definition of V_m
         inline exp_arg real = (V_bounded - V_th) / Delta_T
         inline I_spike pA = g_L * Delta_T * exp(exp_arg)
-        inline I_syn_exc pA = g_exc / nS * pA
-        inline I_syn_inh pA = g_inh / nS * pA
+        # inline I_syn_exc pA = g_exc / nS * pA
+        # inline I_syn_inh pA = g_inh / nS * pA
 
-        V_m' = is_refractory * (-g_L * (V_bounded - E_L) + I_spike + I_syn_exc * (V_bounded - E_exc) * nS / pA - I_syn_inh * (V_bounded - E_exc) * nS / pA - w + I_e + I_stim) / C_m
+        V_m' = (-g_L * (V_bounded - E_L) + I_spike + g_exc * (V_bounded - E_exc) - g_inh * (V_bounded - E_exc) - w + I_e + I_stim) / C_m
         w' = (a * (V_bounded - E_L) - w) / tau_w
 
+        refr_t' = -1e3 * ms/s    # refractoriness is implemented as an ODE, representing a timer counting back down to zero. XXX: TODO: This should simply read ``refr_t' = -1 / s`` (see https://github.com/nest/nestml/issues/984)
+
     parameters:
         # membrane parameters
         C_m pF = 281.0 pF         # Membrane Capacitance
@@ -100,28 +101,22 @@ model aeif_psc_alpha_neuron:
         spike
 
     update:
-        if is_refractory == 1:
+        if refr_t > 0 ms:
             # neuron is absolute refractory, do not evolve V_m
-            refr_t -= resolution()
-
-        # always evolve all ODEs; V_m 
-        integrate_odes()
+            integrate_odes(g_exc, g_inh, w, refr_t)
+        else:
+            # neuron not refractory
+            integrate_odes(g_exc, g_inh, V_m, w)
 
     onReceive(exc_spikes):
         g_exc' += exc_spikes * (e / tau_exc) * nS * s
 
     onReceive(inh_spikes):
         g_inh' += inh_spikes * (e / tau_inh) * nS * s
 
-    onCondition(V_m >= V_th):
+    onCondition(refr_t <= 0 ms and V_m >= V_th):
         # threshold crossing
         refr_t = refr_T    # start of the refractory period
-        is_refractory = 1
         V_m = V_reset
         w += b
         emit_spike()
-
-    onCondition(is_refractory == 1 and refr_t <= resolution() / 2):
-        # end of refractory period
-        refr_t = 0 ms
-        is_refractory = 0

tests/nest_gpu_tests/nest_gpu_code_generator_test.py

@@ -38,7 +38,8 @@ def test_nest_gpu_code_generator_analytic(self):
                                  suffix=suffix)
 
     def test_nest_gpu_code_generator_numeric(self):
-        model_files = ["aeif_psc_exp_neuron.nestml"]
+        # model_files = ["aeif_psc_exp_neuron.nestml"]
+        model_files = ["aeif_cond_alpha_alt_neuron.nestml"]
         input_path = [os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.join(
             os.pardir, os.pardir, "models", "neurons", model)))) for model in model_files]
         target_path = "target_gpu_numeric"