more on the evoked/poisson ampa/nmda updates

L2_basket.py

@@ -159,6 +159,9 @@ def parreceive_ext(self, type, gid, gid_dict, pos_dict, p_ext):
                 # connections depend on location of input - why only for L2 basket and not L5 basket?
                 if p_ext['loc'] is 'proximal':
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.soma_ampa))
+                    # NEW: note that default/original is 0 nmda weight for the soma (for prox evoked)
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.soma_nmda))
+
                 elif p_ext['loc'] is 'distal':
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.soma_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.soma_nmda))

L2_pyramidal.py

@@ -469,6 +469,12 @@ def parreceive_ext (self, type, gid, gid_dict, pos_dict, p_ext):
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.basal2_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.basal3_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.apicaloblique_ampa))
+
+                    # NEW: note that default/original is 0 nmda weight for these proximal dends
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.basal2_nmda))
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.basal3_nmda))
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.apicaloblique_nmda))
+
                 elif p_ext['loc'] is 'distal':
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.apicaltuft_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.apicaltuft_nmda))

L5_basket.py

@@ -144,7 +144,7 @@ def parreceive(self, gid, gid_dict, pos_dict, p_ext):
     # one parreceive function to handle all types of external parreceives
     # types must be defined explicitly here
     def parreceive_ext(self, type, gid, gid_dict, pos_dict, p_ext):
-        if type.startswith(('evprox', 'evdist')):
+        if type.startswith(('evprox', 'evdist')): # shouldn't this just check for evprox?
             if self.celltype in p_ext.keys():
                 gid_ev = gid + gid_dict[type][0]
 
@@ -167,7 +167,9 @@ def parreceive_ext(self, type, gid, gid_dict, pos_dict, p_ext):
                 }
 
                 self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.soma_ampa))
-                #self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.soma_nmda))
+
+                # NEW: note that default/original is 0 nmda weight for the soma (both prox and distal evoked)
+                self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.soma_nmda))
 
         elif type == 'extgauss':
             # gid is this cell's gid

L5_pyramidal.py

@@ -570,6 +570,12 @@ def parreceive_ext(self, type, gid, gid_dict, pos_dict, p_ext):
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.basal2_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.basal3_ampa))
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.apicaloblique_ampa))
+
+                    # NEW: note that default/original is 0 nmda weight for these proximal dends
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.basal2_nmda))
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.basal3_nmda))
+                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_nmda, self.apicaloblique_nmda))
+
                 elif p_ext['loc'] is 'distal':
                     # apical tuft
                     self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict_ampa, self.apicaltuft_ampa))

paramrw.py

@@ -566,7 +566,7 @@ def feed_validate(p_ext, d, tstop):
 
 #
 def checkevokedsynkeys (p, nprox, ndist):
-  # make sure ampa,nmda gbar values are in the param dict (for backwards compatibility)
+  # make sure ampa,nmda gbar values are in the param dict for evoked inputs(for backwards compatibility)
   lctprox = ['L2Pyr','L5Pyr','L2Basket','L5Basket'] # evoked distal target cell types
   lctdist = ['L2Pyr','L5Pyr','L2Basket'] # evoked proximal target cell types
   lsy = ['ampa','nmda'] # synapse types used in evoked inputs
@@ -581,6 +581,19 @@ def checkevokedsynkeys (p, nprox, ndist):
           if k not in p: 
             p[k] = p['gbar_'+skey+'_'+ct]
 
+#
+def checkpoissynkeys (p, nprox, ndist):
+  # make sure ampa,nmda gbar values are in the param dict for Poisson inputs (for backwards compatibility)
+  lct = ['L2Pyr','L5Pyr','L2Basket','L5Basket'] # target cell types
+  lsy = ['ampa','nmda'] # synapse types used in Poisson inputs
+  for ct in lct:
+    for sy in lsy:
+      skey = pref + str(i+1)
+      k = 'gbar_'+skey+'_'+ct+'_'+sy
+      # if the synapse-specific weight not present, set it to 0 in p
+      if k not in p: 
+        p[k] = p['gbar_'+skey+'_'+ct]
+
 # creates the external feed params based on individual simulation params p
 def create_pext (p, tstop):
     # indexable py list of param dicts for parallel
@@ -645,7 +658,9 @@ def create_pext (p, tstop):
     nprox, ndist = countEvokedInputs(p)
     # print('nprox,ndist evoked inputs:', nprox, ndist)
 
-    # make sure all evoked synaptic weights present (for backwards compatibility)
+    # NEW: make sure all evoked synaptic weights present (for backwards compatibility)
+    # could cause differences between output of param files since some nmda weights should
+    # be 0 while others > 0
     checkevokedsynkeys(p,nprox,ndist) 
 
     # Create proximal evoked response parameters
@@ -685,12 +700,12 @@ def create_pext (p, tstop):
 
     # this needs to create many feeds
     # (amplitude, delay, mu, sigma). ordered this way to preserve compatibility
-    p_unique['extgauss'] = { # note double weight specification since only use ampa for gauss inputs
+    p_unique['extgauss'] = { # NEW: note double weight specification since only use ampa for gauss inputs
         'stim': 'gaussian',
-        'L2_basket': (p['L2Basket_Gauss_A_weight'],p['L2Basket_Gauss_A_weight'], 1., p['L2Basket_Gauss_mu'], p['L2Basket_Gauss_sigma']),
-        'L2_pyramidal': (p['L2Pyr_Gauss_A_weight'],p['L2Pyr_Gauss_A_weight'], 0.1, p['L2Pyr_Gauss_mu'], p['L2Pyr_Gauss_sigma']),
-        'L5_basket': (p['L5Basket_Gauss_A_weight'],p['L5Basket_Gauss_A_weight'], 1., p['L5Basket_Gauss_mu'], p['L5Basket_Gauss_sigma']),
-        'L5_pyramidal': (p['L5Pyr_Gauss_A_weight'],p['L5Pyr_Gauss_A_weight'], 1., p['L5Pyr_Gauss_mu'], p['L5Pyr_Gauss_sigma']),
+        'L2_basket':(p['L2Basket_Gauss_A_weight'],p['L2Basket_Gauss_A_weight'],1.,p['L2Basket_Gauss_mu'],p['L2Basket_Gauss_sigma']),
+        'L2_pyramidal':(p['L2Pyr_Gauss_A_weight'],p['L2Pyr_Gauss_A_weight'],0.1,p['L2Pyr_Gauss_mu'],p['L2Pyr_Gauss_sigma']),
+        'L5_basket':(p['L5Basket_Gauss_A_weight'],p['L5Basket_Gauss_A_weight'],1.,p['L5Basket_Gauss_mu'],p['L5Basket_Gauss_sigma']),
+        'L5_pyramidal':(p['L5Pyr_Gauss_A_weight'],p['L5Pyr_Gauss_A_weight'],1.,p['L5Pyr_Gauss_mu'],p['L5Pyr_Gauss_sigma']),
         'lamtha': 100.,
         'prng_seedcore': int(p['prng_seedcore_extgauss']),
         'loc': 'proximal',
@@ -701,12 +716,12 @@ def create_pext (p, tstop):
     if p['T_pois'] in (0, -1): p['T_pois'] = tstop
 
     # Poisson distributed inputs to proximal
-    p_unique['extpois'] = {# note double weight specification since only use ampa for poisson inputs
+    p_unique['extpois'] = {# NEW: setting up AMPA and NMDA for Poisson inputs; why delays differ?
         'stim': 'poisson',
-        'L2_basket': (p['L2Basket_Pois_A_weight'],p['L2Basket_Pois_A_weight'], 1., p['L2Basket_Pois_lamtha']),
-        'L2_pyramidal': (p['L2Pyr_Pois_A_weight'],p['L2Pyr_Pois_A_weight'], 0.1, p['L2Pyr_Pois_lamtha']),
-        'L5_basket': (p['L5Basket_Pois_A_weight'],p['L5Basket_Pois_A_weight'], 1., p['L5Basket_Pois_lamtha']),
-        'L5_pyramidal': (p['L5Pyr_Pois_A_weight'],p['L5Pyr_Pois_A_weight'], 1., p['L5Pyr_Pois_lamtha']),
+        'L2_basket': (p['L2Basket_Pois_A_weight_ampa'],p['L2Basket_Pois_A_weight_nmda'],1.,p['L2Basket_Pois_lamtha']),
+        'L2_pyramidal': (p['L2Pyr_Pois_A_weight_ampa'],p['L2Pyr_Pois_A_weight_nmda'], 0.1,p['L2Pyr_Pois_lamtha']),
+        'L5_basket': (p['L5Basket_Pois_A_weight_ampa'],p['L5Basket_Pois_A_weight_nmda'],1.,p['L5Basket_Pois_lamtha']),
+        'L5_pyramidal': (p['L5Pyr_Pois_A_weight_ampa'],p['L5Pyr_Pois_A_weight_nmda'],1.,p['L5Pyr_Pois_lamtha']),
         'lamtha_space': 100.,
         'prng_seedcore': int(p['prng_seedcore_extpois']),
         't_interval': (p['t0_pois'], p['T_pois']),

params_default.py

@@ -30,28 +30,32 @@ def get_params_default (nprox = 2, ndist = 1):
         'L2Basket_Gauss_A_weight': 0.,
         'L2Basket_Gauss_mu': 2000.,
         'L2Basket_Gauss_sigma': 3.6,
-        'L2Basket_Pois_A_weight': 0.,
+        'L2Basket_Pois_A_weight_ampa': 0.,
+        'L2Basket_Pois_A_weight_nmda': 0.,
         'L2Basket_Pois_lamtha': 0.,
 
         # L2 Pyr params
         'L2Pyr_Gauss_A_weight': 0.,
         'L2Pyr_Gauss_mu': 2000.,
         'L2Pyr_Gauss_sigma': 3.6,
-        'L2Pyr_Pois_A_weight': 0.,
+        'L2Pyr_Pois_A_weight_ampa': 0.,
+        'L2Pyr_Pois_A_weight_nmda': 0.,
         'L2Pyr_Pois_lamtha': 0.,
 
         # L5 Pyr params
         'L5Pyr_Gauss_A_weight': 0.,
         'L5Pyr_Gauss_mu': 2000.,
         'L5Pyr_Gauss_sigma': 4.8,
-        'L5Pyr_Pois_A_weight': 0.,
+        'L5Pyr_Pois_A_weight_ampa': 0.,
+        'L5Pyr_Pois_A_weight_nmda': 0.,
         'L5Pyr_Pois_lamtha': 0.,
 
         # L5 Basket params
         'L5Basket_Gauss_A_weight': 0.,
         'L5Basket_Gauss_mu': 2000.,
         'L5Basket_Gauss_sigma': 2.,
-        'L5Basket_Pois_A_weight': 0.,
+        'L5Basket_Pois_A_weight_ampa': 0.,
+        'L5Basket_Pois_A_weight_nmda': 0.,
         'L5Basket_Pois_lamtha': 0.,
 
         # maximal conductances for all synapses