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, mode=NormalMode)[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, mode=NormalMode)[source]#

Methods

__init__(size[, tau_e, tau_i, beta_e, ...])

clear_input()

Function to clear inputs in the neuron group.

get_batch_shape([batch_size])

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

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(tdi[, x])

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

varshape

The shape of variables in the neuron group.