PoissonInput#

class brainpy.dyn.PoissonInput(target_var, num_input, freq, weight, mode=None, name=None, seed=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. Should be an instance of Variable.

  • num_input (int) – The number of inputs.

  • freq (Union[int, float]) – The frequency of each of the inputs. Must be a scalar.

  • weight (Union[int, float]) – The synaptic weight. Must be a scalar.

  • name (Optional[str]) – The target name.

  • mode (Optional[Mode]) – The computing mode.

reset_state(*args, **kwargs)[source]#

Reset function which resets local states in this model.

Simply speaking, this function should implement the logic of resetting of local variables in this node.

See https://brainpy.readthedocs.io/en/latest/tutorial_toolbox/state_resetting.html for details.

update()[source]#

The function to specify the updating rule.