| Literature DB >> 31163197 |
Abstract
Neuronal firing rate is traditionally defined as the number of spikes per time window. The concept is essential for the rate coding hypothesis, which is still the most commonly investigated scenario in neuronal activity analysis. The estimation of dynamically changing firing rate from neural data can be challenging due to the variability of spike times, even under identical external conditions; hence a wide range of statistical measures have been employed to solve this particular problem. In this paper, we review established firing rate estimation methods, briefly summarize the technical aspects of each approach and discuss their practical applications.Keywords: Bayesian rule; Firing rate; Kernel smoothing; Spike train; Time histogram
Mesh:
Year: 2019 PMID: 31163197 DOI: 10.1016/j.biosystems.2019.103980
Source DB: PubMed Journal: Biosystems ISSN: 0303-2647 Impact factor: 1.973