firing_rate.rst

Firing rate (firing_rate)

Calculation

Firing rate is simply the average number of spikes within the recording per second.

\textrm{firing rate} = \frac{N_s}{T_r}

• N_s : number of spikes observed in full recording.
• T_r : duration of recording in seconds.

Expectation and use

Both very high and very low firing rates can indicate errors. Highly contaminated units (type I error) may have high firing rates as a result of the inclusion of other neurons' spikes. Low firing rate units are likely to be incomplete (type II error), although this is not always the case (some neurons have highly selective firing patterns). The firing rate is expected to be approximately log-normally distributed [Buzsáki]_.

Example code

Without SpikeInterface:

spike_train = ...
t_recording = ...    # Length of recording (in s).

firing_rate = len(spike_train) / t_recording

With SpikeInterface:

import spikeinterface.qualitymetrics as qm

# Make recording, sorting and wvf_extractor object for your data.
firing_rate = qm.compute_firing_rates(wvf_extractor)
# firing_rate is a dict containing the units' IDs as keys,
# and their firing rates across segments as values (in Hz).

References

.. autofunction:: spikeinterface.qualitymetrics.misc_metrics.compute_firing_rates

Links to original implementations

• From the AllenSDK

Literature

Unknown origin. Widely discussed eg: [Buzsáki]_.