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 sqm

# Combine a sorting and recording into a sorting_analyzer
firing_rate = sqm.compute_firing_rates(sorting_analyzer)
# firing_rate is a dict containing the unit IDs as keys,
# and their firing rates across segments as values (in Hz).

References

spikeinterface.qualitymetrics.misc_metrics.compute_firing_rates(sorting_analyzer, unit_ids=None, **kwargs)

Compute the firing rate across segments.

Parameters
sorting_analyzer: SortingAnalyzer

A SortingAnalyzer object

unit_idslist or None

The list of unit ids to compute the firing rate. If None, all units are used.

Returns
firing_ratesdict of floats

The firing rate, across all segments, for each unit ID.

Literature

Unknown origin. Widely discussed eg: [Buzsáki].