PoissonInput class

(Shortest import: from brian2 import PoissonInput)

class brian2.input.poissoninput.PoissonInput(target, target_var, N, rate, weight, when='synapses', order=0)

Bases: brian2.groups.group.CodeRunner

Adds independent Poisson input to a target variable of a Group. 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 Group receive independent realizations of Poisson spike trains.

Parameters

target : Group

The group that is targeted by this input.

target_var : str

The variable of target that is targeted by this input.

N : int

The number of inputs

rate : Quantity

The rate of each of the inputs

weight : str or Quantity

Either a string expression (that can be interpreted in the context of target) or a Quantity that will be added for every event to the target_var of target. The unit has to match the unit of target_var

when : str, optional

When to update the target variable during a time step. Defaults to the synapses scheduling slot.

order : int, optional

The priority of of the update compared to other operations occurring at the same time step and in the same scheduling slot. Defaults to 0.

Attributes

N	The number of inputs
rate	The rate of each input
target_var	The targetted variable
weight	The synaptic weight

Methods

before_run(run_namespace)

Optional method to prepare the object before a run.

Details

N

The number of inputs

rate

The rate of each input

target_var

The targetted variable

weight

The synaptic weight

before_run(run_namespace)

Optional method to prepare the object before a run.

Called by Network.after_run before the main simulation loop starts.

Tutorials and examples using this

• Example frompapers/Rossant_et_al_2011bis

• Example frompapers/Brunel_Wang_2001