def set_linearized_response(self):
"""
Set the linearized response, which only uses the learned background.
This is the matrix used for CS decoding.
"""
# The linear response is scaled same as the activity.
# Ignore the threshold here: assume that system thinks everything
# is firing.
self.Rr = linear_gain(self.Ss0, self.Kk1, self.Kk2, self.eps,
self.binding_competitive, self.num_binding_sites)
# Apply temporal kernel
if self.temporal_run == False:
pass
#self.Rr *= self.kernel_scale*(1 - 2*self.kernel_alpha)
else:
kernel_params = [
self.kernel_T, self.kernel_dt, self.kernel_tau_1,
self.kernel_tau_2, self.kernel_shape_1, self.kernel_shape_2,
self.kernel_alpha, self.kernel_scale
]
self.Rr, self.memory_Rr = temporal_kernel(self.Rr, self.memory_Rr,
self.signal_trace_Tt,
kernel_params)
# Apply nonlinear scaling (ignore threshold)
self.Rr *= self.NL_scale
if self.divisive_normalization == True:
self.Rr = inhibitory_normalization_linear_gain(
self.Yy0, self.Rr, self.inh_C, self.inh_D, self.inh_eta,
self.inh_R)
def set_linearized_response(self):
"""
Set the linearized response, which only uses the learned background.
This is the matrix used for CS decoding.
"""
self.Rr = linear_gain(self.Ss0, self.Kk1, self.Kk2, self.eps)
def set_linearized_response(self):
"""
Set the linearized response, which only uses the learned background.
This is the matrix used for CS decoding.
"""
self.Rr = linear_gain(self.Ss0, self.Kk1, self.Kk2, self.eps)
if self.divisive_normalization == True:
self.Rr = inhibitory_normalization_linear_gain(
self.Yy0, self.Rr, self.inh_C, self.inh_D, self.inh_eta,
self.inh_R)
def set_linearized_response(self): # Linearized response can only use the learned background, or ignore # that knowledge self.Rr = linear_gain(self.Ss0, self.Kk1, self.Kk2, self.eps)