Command: poisson_generator

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()->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: poisson_generator_ps Device parrot_neuron

Source:
/home/abuild/rpmbuild/BUILD/nest-2.4.1/models/poisson_generator.h

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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()->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:	`poisson_generator_ps` `Device` `parrot_neuron`
Source:	/home/abuild/rpmbuild/BUILD/nest-2.4.1/models/poisson_generator.h