Influence of firing mechanisms on gain modulation

TL;DR

This paper investigates how a dynamical threshold affects the firing rate-input relationship in neurons, showing that it enables models to better replicate cortical neuron behavior and influences their computational properties.

Contribution

It demonstrates that incorporating a dynamical threshold into the leaky integrate-and-fire model accurately reproduces cortical neuron f-I curves and reveals its role in neuronal adaptation.

Findings

Dynamical threshold improves model fit to cortical neuron data

It modulates the onset and asymptotic behavior of the f-I curve

Suggests a role for adaptation mechanisms in neurons

Abstract

We studied the impact of a dynamical threshold on the f-I curve-the relationship between the input and the firing rate of a neuron-in the presence of background synaptic inputs. First, we found that, while the leaky integrate-and-fire model cannot reproduce the f-I curve of a cortical neuron, the leaky integrate-and-fire model with dynamical threshold can reproduce it very well. Second, we found that the dynamical threshold modulates the onset and the asymptotic behavior of the f-I curve. These results suggest that a cortical neuron has an adaptation mechanism and that the dynamical threshold has some significance for the computational properties of a neuron.