poisson_generator – Generate spikes with Poisson process statistics

Description

The poisson_generator simulates a neuron that is firing with Poisson statistics, that is, exponentially distributed interspike intervals. It will generate a unique spike train for each of its targets. If you do not want this behavior and need the same spike train for all targets, you have to use a parrot_neuron between the poisson generator and the targets.

All stimulation devices share the parameters start and stop, which control the stimulation period. The property origin is a global offset that shifts the stimulation period. All three values are set as times in ms.

• For spike-emitting devices, only spikes with times t that fulfill \(\mathrm{start} < t \leq \mathrm{stop}\) are emitted. Spikes that have timestamp of \(t = \mathrm{start}\) are not emitted.

• For current-emitting devices, the current is activated and deactivated such that the current first affects the target dynamics during the update step \((\mathrm{start}, \mathrm{start}+h]\), that is, an effect can be recorded at the earliest at time \(\mathrm{start}+h\). The last interval during which the current affects the target’s dynamics is \((\textrm{stop}-h, \textrm{stop}]\).

The property stimulus_source defaults to an empty string. It can be set to the name of a stimulation backend, in which case it will take its parameters from the configured backend instead of from the internally stored values. More details on available backends and their properties can be found in the guide to stimulating the network.

Parameters

label

A string specifying an arbitrary textual label for the device. Stimulation backends might use the label to generate device specific identifiers like filenames and such. Default: "".

origin

A positive floating point number used as the reference time in ms for start and stop. Default: 0.0.

start

A positive floating point number specifying the activation time in ms, relative to origin. Default: 0.0.

stimulus_source

A string specifying the name of the stimulation backend from which to get the data for updating the stimulus parameters of the device. By default, the device uses its internally stored parameters for updating the stimulus. Default: "".

stop

A floating point number specifying the deactivation time in ms, relative to origin. The value of stop must be greater than or equal to start. Default: infinity.

rate

Mean firing rate (spikes/s)

Set parameters from a stimulation backend

The parameters in this stimulation device can be updated with input coming from a stimulation backend. The data structure used for the update holds one value for each of the parameters mentioned above. The indexing is as follows:

0. rate

Sends

SpikeEvent

See also

poisson_generator_ps

Examples using this model

• A model using a single astrocyte with calcium dynamics

• A tripartite interaction between two neurons and one astrocyte

• Balanced neuron example

• Campbell & Siegert approximation example

• Class encapsulating the network for solving Sudoku

• Classes to encapsulate the neuronal networks.

• Clopath Rule: Bidirectional connections

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

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

• Decision making in recurrent network with NMDA-dynamics

• Gap Junctions: Inhibitory network example

• Intrinsic currents spiking

• One neuron with noise

• Population of GIF neuron model with oscillatory behavior

• PyNEST Microcircuit: Network Class

• Random balanced network (alpha synapses) connected with NEST

• Random balanced network (delta synapses)

• Random balanced network (exp synapses, multiple time constants)

• Random balanced network HPC benchmark

• Random balanced network with astrocytes with Bernoulli connectivity

• Random balanced network with astrocytes with fixed-indegree connectivity

• Recording examples

• Repeated Stimulation

• Sensitivity to perturbation

• Sinusoidal poisson generator example

• Store and restore a network simulation

• Structural Plasticity example

• Use evolution strategies to find parameters for a random balanced network (alpha synapses)

• Using CSA for connection setup

• Weight adaptation according to the Urbanczik-Senn plasticity