ignore_and_fire – Ignore-and-fire neuron model for generating spikes at fixed intervals irrespective of inputs


The ignore_and_fire neuron is a neuron model generating spikes at a predefined rate with a constant inter-spike interval (“fire”), irrespective of its inputs (“ignore”). In this simplest version of the ignore_and_fire neuron, the inputs from other neurons or devices are not processed at all.

The model’s state variable, the phase, describes the time to the next spike relative to the firing period (the inverse of the rate). In each update step, the phase is decreased by a fixed amount. If it hits zero, a spike is emitted and the phase is reset to +1.

To create asynchronous activity for a population of ignore_and_fire neurons, the firing phases can be randomly initialized.

The ignore_and_fire neuron is primarily used for neuronal-network model verification and validation purposes (“benchmarking”), in particular, to evaluate the correctness and performance of connectivity generation and inter-neuron communication. It permits an easy scaling of the network size and/or connectivity without affecting the output spike statistics. The amount of network traffic is predefined by the user, and therefore fully controllable and predictable, irrespective of the network size and structure.


The model can easily be extended and equipped with any arbitrary input processing (such as calculating input currents with alpha-function shaped PSC kernels or updating the gating variables in the Hodgkin-Huxley model) or (after-) spike generation dynamics to make it more similar and comparable to other non-ignorant neuron models. In such extended ignore_and_fire models, the spike emission process would still be decoupled from the intrinsic neuron dynamics.


The following parameters can be set in the status dictionary.





Phase (relative time to next spike; 0<phase<=1)



Firing rate




SpikeEvent, CurrentEvent, DataLoggingRequest

Examples using this model