# parrot_neuron – Neuron that repeats incoming spikes¶

## Description¶

The parrot neuron simply emits one spike for every incoming spike.
An important application is to provide identical poisson spike
trains to a group of neurons. The `poisson_generator`

sends a different
spike train to each of its target neurons. By connecting one
`poisson_generator`

to a `parrot_neuron`

and then that `parrot_neuron`

to
a group of neurons, all target neurons will receive the same poisson
spike train.

Please note that weights of connections *to* the `parrot_neuron`

are ignored, while weights on connections *from* the `parrot_neuron`

to the target are handled as usual. Delays are honored on both
incoming and outgoing connections.

Only spikes arriving on connections to port 0 will be repeated.
Connections onto port 1 will be accepted, but spikes incoming
through port 1 will be ignored. This allows setting exact pre-
and postsynaptic spike times for STDP protocols by connecting
two parrot neurons spiking at desired times by, for example, a
`stdp_synapse`

onto port 1 on the postsynaptic parrot neuron.

## Receives¶

SpikeEvent

## Sends¶

SpikeEvent

## Examples using this model¶

Conductance-based generalized leaky integrate and fire (GLIF) neuron example

Current-based generalized leaky integrate and fire (GLIF) neuron example

Tutorial on learning to generate an infinite loop with e-prop

Tutorial on learning to generate handwritten text with e-prop

Weight adaptation according to the Urbanczik-Senn plasticity