**Name:**

poisson_generator - simulate neuron firing with Poisson processes

statistics.

**Description:**

The poisson_generator simulates a neuron that is firing with Poisson

statistics, i.e. exponentially distributed interspike intervals. It will

generate a _unique_ spike train for each of it's targets. If you do not want

this behavior and need the same spike train for all targets, you have to use a

parrot neuron inbetween the poisson generator and the targets.

**Parameters:**

The following parameters appear in the element's status dictionary:

rate double - mean firing rate in Hz

origin double - Time origin for device timer in ms

start double - begin of device application with resp. to origin in ms

stop double - end of device application with resp. to origin in ms

**Sends:**

SpikeEvent

**Remarks:**

A Poisson generator may, especially at high rates, emit more than one

spike during a single time step. If this happens, the generator does

not actually send out n spikes. Instead, it emits a single spike with

n-fold synaptic weight for the sake of efficiency.

The design decision to implement the Poisson generator as a device

which sends spikes to all connected nodes on every time step and then

discards the spikes that should not have happened generating random

numbers at the recipient side via an event hook is twofold.

On one hand, it leads to the saturation of the messaging network with

an enormous amount of spikes, most of which will never get delivered

and should not have been generated in the first place.

On the other hand, a proper implementation of the Poisson generator

needs to provide two basic features: (a) generated spike trains

should be IID processes w.r.t. target neurons to which the generator

is connected and (b) as long as virtual_num_proc is constant, each

neuron should receive an identical Poisson spike train in order to

guarantee reproducibility of the simulations across varying machine

numbers.

Therefore, first, as Network::get_network().send sends spikes to all the

recipients, differentiation has to happen in the hook, second, the

hook can use the RNG from the thread where the recipient neuron sits,

which explains the current design of the generator. For details,

refer to:

http://ken.brainworks.uni-freiburg.de/cgi-bin/mailman/private/nest_developer/2011-January/002977.html

**SeeAlso:**

**Source:**

/home/graber/work-nest/nest-git/nest-simulator/models/poisson_generator.h