PoissonInput class
(Shortest import: from brian2 import PoissonInput)
- class brian2.input.poissoninput.PoissonInput(target, target_var, N, rate, weight, when='synapses', order=0)[source]
Bases:
CodeRunner
Adds independent Poisson input to a target variable of a
Group
. For large numbers of inputs, this is much more efficient than creating aPoissonGroup
. 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 targetGroup
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 aQuantity
that will be added for every event to thetarget_var
oftarget
. The unit has to match the unit oftarget_var
when : str, optional
When to update the target variable during a time step. Defaults to the
synapses
scheduling slot. See Scheduling for possible values.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
The number of inputs
The rate of each input
The targetted variable
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)[source]
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/Brunel_2000
Example frompapers/Brunel_Wang_2001
Example frompapers/Rossant_et_al_2011bis
Example frompapers/Wang_2002