Mean instantaneous firing frequency is always higher than the firing rate

Authors:
Petr Lánský;Roger Rodriguez;Laura Sacerdote
Affiliations:
Institute of Physiology, Academy of Sciences of the Czech Republic, 142 20 Prague 4, Czech Republic;Centre de Physique Théorique, CNRS and Faculté des Sciences de Luminy, Université de la Méditerranée, F-13288 Marseille Cedex 09, France;Department of Mathematics, University of Torino, via Carlo Alberto 10, 10 123 Torino, Italy
Venue:
Neural Computation
Year:
2004

Citing 15
Cited 4

Diffusion approximation of the neuronal model with synaptic reversal potentials

Biological Cybernetics
Continuous functions determined by spike trains of a neuron subject to stimulation

Biological Cybernetics
Analysis of neural excitability and oscillations

Methods in neuronal modeling
Noise adaptation in integrate-and-fire neurons

Neural Computation
Linearization of F-1 curves by adaptation

Neural Computation
Firing rate distributions and efficiency of information transmission of inferior temporal cortex neurons to natural visual stimuli

Neural Computation
Spikes: exploring the neural code

Spikes: exploring the neural code
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Dynamics of the firing probability of noisy integrate-and-fire neurons

Neural Computation
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex

Neural Computation
Determination of Response Latency and Its Application to Normalization of Cross-Correlation Measures

Neural Computation
Analytical Model for the Effects of Learning on Spike Count Distributions

Neural Computation
Calculation of Interspike Intervals for Integrate-and-Fire Neurons with Poisson Distribution of Synaptic Inputs

Neural Computation
An Accurate Measure of the Instantaneous Discharge Probability, with Application to Unitary Joint-Event Analysis

Neural Computation

Optimal Signal Estimation in Neuronal Models

Neural Computation
Stimulus-response curves in sensory neurons: how to find the stimulus measurable with the highest precision

BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
How sample paths of leaky integrate-and-fire models are influenced by the presence of a firing threshold

Neural Computation
Firing variability is higher than deduced from the empirical coefficient of variation

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Frequency coding is considered one of the most common coding strategies employed by neural systems. This fact leads, in experiments as well as in theoretical studies, to construction of so-called transfer functions, where the output firing frequency is plotted against the input intensity. The term firing frequency can be understood differently in different contexts. Basically, it means that the number of spikes over an interval of preselected length is counted and then divided by the length of the interval, but due to the obvious limitations, the length of observation cannot be arbitrarily long. Then firing frequency is defined as reciprocal to the mean interspike interval. In parallel, an instantaneous firing frequency can be defined as reciprocal to the length of current interspike interval, and by taking a mean of these, the definition can be extended to introduce the mean instantaneous firing frequency. All of these definitions of firing frequency are compared in an effort to contribute to a better understanding of the input-output properties of a neuron.