brainpy.dyn.rates.ThresholdLinearModel
brainpy.dyn.rates.ThresholdLinearModel#
- class brainpy.dyn.rates.ThresholdLinearModel(size, tau_e=0.02, tau_i=0.01, beta_e=0.066, beta_i=0.351, noise_e=0.0, noise_i=0.0, e_initializer=ZeroInit, i_initializer=ZeroInit, seed=None, keep_size=False, name=None)[source]#
A threshold linear rate model.
The threshold linear rate model is given by 1
\[\begin{split}\begin{aligned} &\tau_{E} \frac{d \nu_{E}}{d t}=-\nu_{E}+\beta_{E}\left[I_{E}\right]_{+} \\ &\tau_{I} \frac{d \nu_{I}}{d t}=-\nu_{I}+\beta_{I}\left[I_{I}\right]_{+} \end{aligned}\end{split}\]where \(\left[I_{E}\right]_{+}=\max \left(I_{E}, 0\right)\). \(v_E\) and \(v_I\) denote the firing rates of the excitatory and inhibitory populations respectively, \(\tau_E\) and \(\tau_I\) are the corresponding intrinsic time constants.
- 1
Chaudhuri, Rishidev, et al. “A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex.” Neuron 88.2 (2015): 419-431.
- __init__(size, tau_e=0.02, tau_i=0.01, beta_e=0.066, beta_i=0.351, noise_e=0.0, noise_i=0.0, e_initializer=ZeroInit, i_initializer=ZeroInit, seed=None, keep_size=False, name=None)[source]#
Methods
__init__
(size[, tau_e, tau_i, beta_e, ...])get_delay_data
(name, delay_step, *indices)Get delay data according to the provided delay steps.
ints
([method])Collect all integrators in this node and the children nodes.
load_states
(filename[, verbose])Load the model states.
nodes
([method, level, include_self])Collect all children nodes.
register_delay
(name, delay_step, delay_target)Register delay variable.
register_implicit_nodes
(nodes)register_implicit_vars
(variables)reset
()Reset function which reset the whole variables in the model.
reset_delay
(name, delay_target)Reset the delay variable.
save_states
(filename[, variables])Save the model states.
train_vars
([method, level, include_self])The shortcut for retrieving all trainable variables.
unique_name
([name, type_])Get the unique name for this object.
update
(t, dt)The function to specify the updating rule.
update_delay
(name, delay_data)Update the delay according to the delay data.
vars
([method, level, include_self])Collect all variables in this node and the children nodes.
Attributes
global_delay_vars
name
steps