aln: fix index of input from i to i-1 (#240)

neurolib-dev · Aug 30, 2023 · ce434bd · ce434bd
1 parent bec8fbc
commit ce434bd
Showing 1 changed file with 8 additions and 11 deletions.
diff --git a/neurolib/models/aln/timeIntegration.py b/neurolib/models/aln/timeIntegration.py
@@ -396,7 +396,6 @@ def timeIntegration_njit_elementwise(
     noise_exc,
     noise_inh,
 ):
-
     # squared Jee_max
     sq_Jee_max = Jee_max**2
     sq_Jei_max = Jei_max**2
@@ -415,7 +414,6 @@ def timeIntegration_njit_elementwise(
 
     ### integrate ODE system:
     for i in range(startind, startind + len(t)):
-
         if not distr_delay:
             # Get the input from one node into another from the rates at time t - connection_delay - 1
             # remark: assume Kie == Kee and Kei == Kii
@@ -429,13 +427,12 @@ def timeIntegration_njit_elementwise(
 
         # loop through all the nodes
         for no in range(N):
-
             # To save memory, noise is saved in the rates array
             noise_exc[no] = rates_exc[no, i]
             noise_inh[no] = rates_inh[no, i]
 
-            mue = Jee_max * seem[no] + Jei_max * seim[no] + mue_ou[no] + ext_exc_current[no, i]
-            mui = Jie_max * siem[no] + Jii_max * siim[no] + mui_ou[no] + ext_inh_current[no, i]
+            mue = Jee_max * seem[no] + Jei_max * seim[no] + mue_ou[no] + ext_exc_current[no, i - 1]
+            mui = Jie_max * siem[no] + Jii_max * siim[no] + mui_ou[no] + ext_inh_current[no, i - 1]
 
             # compute row sum of Cmat*rd_exc and Cmat**2*rd_exc
             rowsum = 0
@@ -446,20 +443,22 @@ def timeIntegration_njit_elementwise(
 
             # z1: weighted sum of delayed rates, weights=c*K
             z1ee = (
-                cee * Ke * rd_exc[no, no] + c_gl * Ke_gl * rowsum + c_gl * Ke_gl * ext_exc_rate[no, i]
+                cee * Ke * rd_exc[no, no] + c_gl * Ke_gl * rowsum + c_gl * Ke_gl * ext_exc_rate[no, i - 1]
             )  # rate from other regions + exc_ext_rate
             z1ei = cei * Ki * rd_inh[no]
             z1ie = (
-                cie * Ke * rd_exc[no, no] + c_gl * Ke_gl * ext_inh_rate[no, i]
+                cie * Ke * rd_exc[no, no] + c_gl * Ke_gl * ext_inh_rate[no, i - 1]
             )  # first test of external rate input to inh. population
             z1ii = cii * Ki * rd_inh[no]
             # z2: weighted sum of delayed rates, weights=c^2*K (see thesis last ch.)
             z2ee = (
-                cee**2 * Ke * rd_exc[no, no] + c_gl**2 * Ke_gl * rowsumsq + c_gl**2 * Ke_gl * ext_exc_rate[no, i]
+                cee**2 * Ke * rd_exc[no, no]
+                + c_gl**2 * Ke_gl * rowsumsq
+                + c_gl**2 * Ke_gl * ext_exc_rate[no, i - 1]
             )
             z2ei = cei**2 * Ki * rd_inh[no]
             z2ie = (
-                cie**2 * Ke * rd_exc[no, no] + c_gl**2 * Ke_gl * ext_inh_rate[no, i]
+                cie**2 * Ke * rd_exc[no, no] + c_gl**2 * Ke_gl * ext_inh_rate[no, i - 1]
             )  # external rate input to inh. population
             z2ii = cii**2 * Ki * rd_inh[no]
 
@@ -595,7 +594,6 @@ def interpolate_values(table, xid1, yid1, dxid, dyid):
 
 @numba.njit(locals={"idxX": numba.int64, "idxY": numba.int64})
 def lookup_no_interp(x, dx, xi, y, dy, yi):
-
     """
     Return the indices for the closest values for a look-up table
     Choose the closest point in the grid
@@ -638,7 +636,6 @@ def lookup_no_interp(x, dx, xi, y, dy, yi):
 
 @numba.njit(locals={"xid1": numba.int64, "yid1": numba.int64, "dxid": numba.float64, "dyid": numba.float64})
 def fast_interp2_opt(x, dx, xi, y, dy, yi):
-
     """
     Returns the values needed for interpolation:
     - bilinear (2D) interpolation within ranges,