def set_adapted_free_energy(self):
"""
Set free energy based on adapted activity activity.
"""
activity_stats = [
self.adapted_activity_mu, self.adapted_activity_sigma
]
adapted_activity = random_matrix([self.Mm],
params=activity_stats,
seed=self.seed_adapted_activity)
self.eps = free_energy(self.Ss, self.Kk1, self.Kk2, adapted_activity,
self.binding_competitive,
self.num_binding_sites)
# Apply max and min epsilon value to each component
self.min_eps = random_matrix(
self.Mm,
params=[self.mu_min_eps, self.sigma_min_eps],
seed=self.seed_eps)
self.max_eps = random_matrix(
self.Mm,
params=[self.mu_max_eps, self.sigma_max_eps],
seed=self.seed_eps)
self.eps = sp.maximum(self.eps.T, self.min_eps).T
self.eps = sp.minimum(self.eps.T, self.max_eps).T
def set_adapted_free_energy(self): """ Set free energy based on adapted activity activity. """ activity_stats = [self.adapted_activity_mu, self.adapted_activity_sigma] adapted_activity = random_matrix([self.Mm], params=activity_stats, seed=self.seed_adapted_activity) self.eps = free_energy(self.Ss0, self.Kk1, self.Kk2, adapted_activity)
def set_temporal_adapted_epsilon(self):
"""
Set adapted epsilon based on current value and adaptation rate.
The adapted value is set by linear decay rate equation,
d(eps)/dt = beta*(a_0 - a). a_0 is set by passing the manually
chosen variables temporal_adaptation_mu_eps and
temporal_adaptation_mu_Ss0 to the activity.
"""
# Perfectly adapted activity level is set manually
activity_stats = [
self.adapted_activity_mu, self.adapted_activity_sigma
]
perfect_adapt_Yy = random_matrix([self.Mm],
params=activity_stats,
seed=self.seed_adapted_activity)
beta_scale_factors = sp.random.uniform(self.adaptive_beta_scaling_min,
self.adaptive_beta_scaling_max,
self.Mm)
# This is a kind of hacky way to incorporate WL breaking.
# Requires Kk1 to be small
Kk2_sum = sp.dot(self.Kk2**-1.0, self.Ss).T
den = perfect_adapt_Yy.T*(1 - (1/Kk2_sum)**beta_scale_factors)\
+ (1/Kk2_sum)**beta_scale_factors
perfect_adapt_Yy = (perfect_adapt_Yy.T / den)
# Make adaptation rate into a vector if it has not yet been set.
try:
self.temporal_adaptation_rate_vector
except AttributeError:
assert self.temporal_adaptation_rate_sigma == 0, "Before "\
"setting new epsilon with set_temporal_adapted_epsilon, "\
"you must call set_ordered_temporal_adaptation_rate, since "\
"temporal_adaptation_rate_sigma is nonzero"
self.temporal_adaptation_rate_vector = sp.ones(self.Mm)*\
self.temporal_adaptation_rate
# Receptor activity used for adaptation is not firing rate; just
# get the Or/Orco activity, w/o LN functions at backend.
current_Yy = receptor_activity(self.Ss, self.Kk1, self.Kk2, self.eps,
self.binding_competitive,
self.num_binding_sites)
if self.temporal_adaptation_type == 'imperfect':
d_eps_dt = (self.temporal_adaptation_rate_vector*\
(current_Yy.T - perfect_adapt_Yy)).T
delta_t = self.signal_trace_Tt[1] - self.signal_trace_Tt[0]
self.eps += delta_t * d_eps_dt
elif self.temporal_adaptation_type == 'perfect':
self.eps = free_energy(self.Ss, self.Kk1, self.Kk2,
perfect_adapt_Yy)
# Enforce epsilon limits
self.eps = sp.maximum(self.eps.T, self.min_eps).T
self.eps = sp.minimum(self.eps.T, self.max_eps).T
def set_adapted_free_energy(self):
"""
Set free energy based on adapted activity activity.
"""
activity_stats = [
self.adapted_activity_mu, self.adapted_activity_sigma
]
adapted_activity = random_matrix([self.Mm],
params=activity_stats,
seed=self.seed_adapted_activity)
# Break adaptation slightly by adjusting A0 with log of s0
sp.random.seed(self.seed_adapted_activity)
if (self.mu_dSs_2 is not None):
max_sig_comp = max(max(self.mu_dSs, self.mu_dSs_2), self.mu_Ss0)
else:
max_sig_comp = max(self.mu_dSs, self.mu_Ss0)
factors = sp.random.uniform(self.imperfect_A0_mult_min,
self.imperfect_A0_mult_max, self.Mm)
factors *= sp.log(max_sig_comp) / sp.log(10) + self.imperfect_A0_const
adapted_activity *= (1 + factors)
adapted_activity = sp.minimum(adapted_activity, 0.99)
if sp.mean(factors) > 1:
print('activity factors:', sp.mean(factors), '+/-',
sp.std(factors))
# Scale the beta factor instead (breaks WL from 0 to 1)
sp.random.seed(self.seed_adapted_activity)
beta_scale_factors = sp.random.uniform(self.adaptive_beta_scaling_min,
self.adaptive_beta_scaling_max,
self.Mm)
self.eps = free_energy(self.Ss, self.Kk1, self.Kk2, adapted_activity,
self.binding_competitive,
self.num_binding_sites, beta_scale_factors)
self.min_eps = random_matrix(
self.Mm,
params=[self.mu_min_eps, self.sigma_min_eps],
seed=self.seed_eps)
self.max_eps = random_matrix(
self.Mm,
params=[self.mu_max_eps, self.sigma_max_eps],
seed=self.seed_eps)
self.eps = sp.maximum(self.eps.T, self.min_eps).T
self.eps = sp.minimum(self.eps.T, self.max_eps).T
def set_temporal_adapted_epsilon(self):
"""
Set adapted epsilon based on current value and adaptation rate.
The adapted value is set by linear decay rate equation,
d(eps)/dt = beta*(a_0 - a). a_0 is set by passing the manually
chosen variables temporal_adaptation_mu_eps and
temporal_adaptation_mu_Ss0 to the activity.
"""
# Perfectly adapted activity level is based on the variables:
# temporal_adaptation_mu_eps, temporal_adaptation_sigma_eps,
# temporal_adaptation_mu_Ss0. These functions take the activity
# level set by these variables at that signal intensity, to
# adapt epsilon to the current Ss
perfect_adapt_eps_base = sp.ones(self.Mm)*\
self.temporal_adaptation_mu_eps + random_matrix(self.Mm,
params=[0, self.temporal_adaptation_sigma_eps],
seed=self.seed_eps)
perfect_adapt_Ss = sp.zeros(self.Nn)
perfect_adapt_Ss[self.Ss0 != 0] = self.temporal_adaptation_mu_Ss0
perfect_adapt_Yy = receptor_activity(perfect_adapt_Ss, self.Kk1,
self.Kk2, perfect_adapt_eps_base)
# Make adaptation rate into a vector if it has not yet been set.
try:
self.temporal_adaptation_rate_vector
except AttributeError:
assert self.temporal_adaptation_rate_sigma == 0, "Before "\
"setting new epsilon with set_temporal_adapted_epsilon, "\
"you must call set_ordered_temporal_adaptation_rate, since "\
"temporal_adaptation_rate_sigma is nonzero"
self.temporal_adaptation_rate_vector = sp.ones(self.Mm)*\
self.temporal_adaptation_rate
if self.temporal_adaptation_type == 'imperfect':
d_eps_dt = self.temporal_adaptation_rate_vector*\
(self.Yy - perfect_adapt_Yy)
delta_t = self.signal_trace_Tt[1] - self.signal_trace_Tt[0]
self.eps += delta_t * d_eps_dt
elif self.temporal_adaptation_type == 'perfect':
self.eps = free_energy(self.Ss, self.Kk1, self.Kk2,
perfect_adapt_Yy)