brainpy.synapses.PoissonInput#

class brainpy.synapses.PoissonInput(target_var, num_input, freq, weight, seed=None, mode=None, name=None)[source]#

Poisson Input to the given Variable.

Adds independent Poisson input to a target variable. For large numbers of inputs, this is much more efficient than creating a PoissonGroup. The synaptic events are generated randomly during the simulation and are not preloaded and stored in memory. All the inputs must target the same variable, have the same frequency and same synaptic weight. All neurons in the target variable receive independent realizations of Poisson spike trains.

Parameters

target_var (Variable) – The variable that is targeted by this input.
num_input (int) – The number of inputs.
freq (float) – The frequency of each of the inputs. Must be a scalar.
weight (float) – The synaptic weight. Must be a scalar.

__init__(target_var, num_input, freq, weight, seed=None, mode=None, name=None)[source]#

Methods

`__init__`(target_var, num_input, freq, weight)
`check_post_attrs`(*attrs)	Check whether post group satisfies the requirement.
`check_pre_attrs`(*attrs)	Check whether pre group satisfies the requirement.
`clear_input`()
`cpu`()	Move all variable into the CPU device.
`cuda`()	Move all variables into the GPU device.
`get_delay_data`(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
`load_state_dict`(state_dict[, warn])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`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[, node_cls])
`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.
`state_dict`()	Returns a dictionary containing a whole state of the module.
`to`(device)	Moves all variables into the given device.
`tpu`()	Move all variables into the TPU device.
`train_vars`([method, level, include_self])	The shortcut for retrieving all trainable variables.
`tree_flatten`()	Flattens the object as a PyTree.
`tree_unflatten`(aux, dynamic_values)	New in version 2.3.1.
`unique_name`([name, type_])	Get the unique name for this object.
`update`(tdi)	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`	Global delay data, which stores the delay variables and corresponding delay targets.
`mode`	Mode of the model, which is useful to control the multiple behaviors of the model.
`name`	Name of the model.