# Inhibition enhances the coherence in the Jacobi neuronal model

## TL;DR

This paper demonstrates that inhibition can enhance coherence and information transmission in the Jacobi neuronal model by affecting output firing rate, variability, and spectral coherence, emphasizing the importance of spectral analysis.

## Contribution

It reveals how inhibition influences noise-dependent coherence and highlights the significance of power spectrum analysis over traditional metrics.

## Key findings

- Inhibition increases output coherence in the model.
- Maxima in firing rate and diffusion coefficient depend on inhibition rate.
- Power spectrum analysis is crucial for assessing coherence resonance.

## Abstract

The output signal is examined for the Jacobi neuronal model which is characterized by input-dependent multiplicative noise. The dependence of the noise on the rate of inhibition turns out to be of primary importance to observe maxima both in the output firing rate and in the diffusion coefficient of the spike count and, simultaneously, a minimum in the coefficient of variation (Fano factor). Moreover, we observe that an increment of the rate of inhibition can increase the degree of coherence computed from the power spectrum. This means that inhibition can enhance the coherence and thus the information transmission between the input and the output in this neuronal model. Finally, we stress that the firing rate, the coefficient of variation and the diffusion coefficient of the spike count cannot be used as the only indicator of coherence resonance without considering the power spectrum.