Improving the Entropy Estimate of Neuronal Firings of Modeled Cochlear Nucleus Neurons

TL;DR

This paper enhances entropy estimation methods to analyze modeled cochlear nucleus neuron spike trains, revealing insights into neuronal coding precision and response predictability to speech sounds.

Contribution

It introduces a time-varying entropy estimation approach for neuronal spike trains, improving analysis of neuronal response predictability and temporal coding.

Findings

Improved entropy estimates reveal neuronal response predictability.

Analysis links speech frequency content to neuronal coding precision.

Quantifies temporal memory in neuronal responses.

Abstract

In this correspondence information theoretical tools are used to investigate the statistical properties of modeled cochlear nucleus globular bushy cell spike trains. The firing patterns are obtained from a simulation software that generates sample spike trains from any auditory input. Here we analyze for the first time the responses of globular bushy cells to voiced and unvoiced speech sounds. Classical entropy estimates, such as the direct method, are improved upon by considering a time-varying and time-dependent entropy estimate. With this method we investigated the relationship between the predictability of the neuronal response and the frequency content in the auditory signals. The analysis quantifies the temporal precision of the neuronal coding and the memory in the neuronal response.