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relabelling "del" => "delayed" in aln model
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for consistency
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@lenasal
lenasal authored Feb 15, 2024
1 parent e85171e commit 2736c75
Showing 1 changed file with 54 additions and 54 deletions.
108 changes: 54 additions & 54 deletions neurolib/models/aln/timeIntegration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -732,8 +732,8 @@ def jacobian_aln(
uri,
nw_input,
nw_input_sq,
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
):
"""Jacobian of the ALN dynamical system.
Expand All @@ -754,10 +754,10 @@ def jacobian_aln(
:type nw_input: float
:param nw_input_sq: sum of all network inputs into current node at current time with squared prefactors
:type nw_input_sq: float
:param re_del: E rate delayed by de
:type re_del: float
:param ri_del: I rate delayed by di
:type ri_del: float
:param re_delayed: E rate delayed by de
:type re_delayed: float
:param ri_delayed: I rate delayed by di
:type ri_delayed: float
:param sv: dictionary of state vars and respective indices
:type sv: dict
Expand Down Expand Up @@ -806,15 +806,15 @@ def jacobian_aln(

jacobian = np.zeros((V, V))

z1ee = z1ee_f * re_del + nw_input + c_gl * Ke_gl * ure
z2ee = z2ee_f * re_del + nw_input_sq + c_gl**2 * Ke_gl * ure
z1ei = z1ei_f * ri_del
z2ei = z2ei_f * ri_del
z1ee = z1ee_f * re_delayed + nw_input + c_gl * Ke_gl * ure
z2ee = z2ee_f * re_delayed + nw_input_sq + c_gl**2 * Ke_gl * ure
z1ei = z1ei_f * ri_delayed
z2ei = z2ei_f * ri_delayed

z1ie = z1ie_f * re_del + c_gl * Ke_gl * uri
z2ie = z2ie_f * re_del + c_gl**2 * Ke_gl * uri
z1ii = z1ii_f * ri_del
z2ii = z2ii_f * ri_del
z1ie = z1ie_f * re_delayed + c_gl * Ke_gl * uri
z2ie = z2ie_f * re_delayed + c_gl**2 * Ke_gl * uri
z1ii = z1ii_f * ri_delayed
z2ii = z2ii_f * ri_delayed

sig_ee_den = (1 + z1ee) * taum + tau_se
sig_ei_den = (1 + z1ei) * taum + tau_si
Expand Down Expand Up @@ -1004,7 +1004,7 @@ def compute_hx(
ui = control[n, sv["rates_inh"], t]
ure = control[n, sv["mufe"], t]
uri = control[n, sv["mufi"], t]
re_del, ri_del = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
re_delayed, ri_delayed = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
hx[n, t, :, :] = jacobian_aln(
model_params,
precomp_factors,
Expand All @@ -1016,8 +1016,8 @@ def compute_hx(
uri,
nw_input[n, t],
nw_input_sq[n, t],
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
)

Expand Down Expand Up @@ -1056,8 +1056,8 @@ def jacobian_de(
ure,
uri,
nw_input,
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
):
"""Jacobian of the ALN dynamical system wrt relations with delay de
Expand All @@ -1076,10 +1076,10 @@ def jacobian_de(
:type ui: float
:param nw_input: sum of all network inputs into current node at current time
:type nw_input: float
:param re_del: E rate delayed by de
:type re_del: float
:param ri_del: I rate delayed by di
:type ri_del: float
:param re_delayed: E rate delayed by de
:type re_delayed: float
:param ri_delayed: I rate delayed by di
:type ri_delayed: float
:param sv: dictionary of state vars and respective indices
:type sv: dict
Expand Down Expand Up @@ -1127,10 +1127,10 @@ def jacobian_de(

jacobian = np.zeros((V, V))

z1ee = z1ee_f * re_del + nw_input + c_gl * Ke_gl * ure # factors 1e-3 are in z1ee_f and in ne_input
z1ei = z1ei_f * ri_del
z1ie = z1ie_f * re_del + c_gl * Ke_gl * uri
z1ii = z1ii_f * ri_del
z1ee = z1ee_f * re_delayed + nw_input + c_gl * Ke_gl * ure # factors 1e-3 are in z1ee_f and in ne_input
z1ei = z1ei_f * ri_delayed
z1ie = z1ie_f * re_delayed + c_gl * Ke_gl * uri
z1ii = z1ii_f * ri_delayed

sig_ee_den = (1 + z1ee) * taum + tau_se
sig_ei_den = (1 + z1ei) * taum + tau_si
Expand Down Expand Up @@ -1283,7 +1283,7 @@ def compute_hx_de(
ui = control[n, sv["rates_inh"], t]
ure = control[n, sv["mufe"], t]
uri = control[n, sv["mufi"], t]
re_del, ri_del = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
re_delayed, ri_delayed = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
hx[n, t, :, :] = jacobian_de(
model_params,
precomp_factors,
Expand All @@ -1294,8 +1294,8 @@ def compute_hx_de(
ure,
uri,
nw_input[n, t],
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
)

Expand All @@ -1313,8 +1313,8 @@ def jacobian_di(
ure,
uri,
nw_input,
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
):
"""Jacobian of the ALN dynamical system wrt relations with delay di
Expand All @@ -1332,10 +1332,10 @@ def jacobian_di(
:type ui: float
:param nw_input: sum of all network inputs into current node at current time
:type nw_input: float
:param re_del: E rate delayed by de
:type re_del: float
:param ri_del: I rate delayed by di
:type ri_del: float
:param re_delayed: E rate delayed by de
:type re_delayed: float
:param ri_delayed: I rate delayed by di
:type ri_delayed: float
:param sv: dictionary of state vars and respective indices
:type sv: dict
Expand Down Expand Up @@ -1383,10 +1383,10 @@ def jacobian_di(

jacobian = np.zeros((V, V))

z1ee = z1ee_f * re_del + nw_input + c_gl * Ke_gl * ure
z1ei = z1ei_f * ri_del
z1ie = z1ie_f * re_del + c_gl * Ke_gl * uri
z1ii = z1ii_f * ri_del
z1ee = z1ee_f * re_delayed + nw_input + c_gl * Ke_gl * ure
z1ei = z1ei_f * ri_delayed
z1ie = z1ie_f * re_delayed + c_gl * Ke_gl * uri
z1ii = z1ii_f * ri_delayed

sig_ee_den = (1 + z1ee) * taum + tau_se
sig_ei_den = (1 + z1ei) * taum + tau_si
Expand Down Expand Up @@ -1541,7 +1541,7 @@ def compute_hx_di(
ui = control[n, sv["rates_inh"], t]
ure = control[n, sv["mufe"], t]
uri = control[n, sv["mufi"], t]
re_del, ri_del = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
re_delayed, ri_delayed = dyn_vars[n, sv["rates_exc"], t - ndt_de], dyn_vars[n, sv["rates_inh"], t - ndt_di]
hx[n, t, :, :] = jacobian_di(
model_params,
precomp_factors,
Expand All @@ -1552,8 +1552,8 @@ def compute_hx_di(
ure,
uri,
nw_input[n, t],
re_del,
ri_del,
re_delayed,
ri_delayed,
sv,
)

Expand Down Expand Up @@ -1617,16 +1617,16 @@ def compute_hx_nw(
if cmat[n1, n2] == 0.0:
continue
for t in range(T):
re_del, ri_del = dyn_vars[n1, sv["rates_exc"], t - ndt_de], dyn_vars[n1, sv["rates_inh"], t - ndt_di]
re_delayed, ri_delayed = dyn_vars[n1, sv["rates_exc"], t - ndt_de], dyn_vars[n1, sv["rates_inh"], t - ndt_di]
ue = control[n1, sv["rates_exc"], t]
ure = control[n1, sv["mufe"], t]
hx_nw[n1, n2, t, :, :] = jacobian_nw(
model_params,
precomp_factors,
V,
dyn_vars[n1, :, t],
re_del,
ri_del,
re_delayed,
ri_delayed,
nw_input[n1, t],
cmat[n1, n2],
ue,
Expand All @@ -1643,8 +1643,8 @@ def jacobian_nw(
precomp_factors,
V,
fullstate,
re_del,
ri_del,
re_delayed,
ri_delayed,
nw_input,
cmat_entry,
ue,
Expand All @@ -1661,10 +1661,10 @@ def jacobian_nw(
:type V: int
:param fullstate: Value of all V=16 dynamical variables at given time
:type fullstate: np.ndarray
:param re_del: E rate delayed by de
:type re_del: float
:param ri_del: I rate delayed by di
:type ri_del: float
:param re_delayed: E rate delayed by de
:type re_delayed: float
:param ri_delayed: I rate delayed by di
:type ri_delayed: float
:param nw_input: sum of all network inputs into current node at current time
:type nw_input: float
:param cmat_entry: Entry of the connectivity matrix at n1, n2
Expand Down Expand Up @@ -1719,8 +1719,8 @@ def jacobian_nw(

jac_nw = np.zeros((V, V))

z1ee = z1ee_f * re_del + nw_input + c_gl * Ke_gl * ure
z1ei = z1ei_f * ri_del
z1ee = z1ee_f * re_delayed + nw_input + c_gl * Ke_gl * ure
z1ei = z1ei_f * ri_delayed

sig_ee_den = (1 + z1ee) * taum + tau_se
sig_ei_den = (1 + z1ei) * taum + tau_si
Expand Down

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