The effect of input noises on the activity of auditory neurons using GLM-based metrics ^*

Abstract

Objective. The auditory system is extremely efficient in extracting auditory information in the presence of background noise. However, people with auditory implants have a hard time understanding speech in noisy conditions. The neural mechanisms related to the processing of background noise, especially in the inferior colliculus (IC) where the auditory midbrain implant is located, are still not well understood. Understanding the mechanisms of perception in noise could lead to better stimulation or preprocessing strategies for such implants. We thus wish to investigate if there is a difference in the activity of neurons in the IC when presenting noisy vocalizations with different types of noise (stationary vs. non-stationary), input signal-to-noise ratios (SNR) and signal levels. Approach. We developed novel metrics based on a generalized linear model (GLM) to investigate the effect of a given input noise on neural activity. We used these metrics to analyze neural data recorded from the IC in ketamine-anesthetized female Hartley guinea pigs while presenting noisy vocalizations. Main results. We found that non-stationary noise clearly contributes to the multi-unit neural activity in the IC by causing excitation, regardless of the SNR, input level or vocalization type. However, when presenting white or natural stationary noises, a great diversity of responses was observed for the different conditions, where the multi-unit activity of some sites was affected by the presence of noise and the activity of others was not. Significance. The GLM-based metrics allowed the identification of a clear distinction between the effect of white or natural stationary noises and that of non-stationary noise on the multi-unit activity in the IC. This had not been observed before and indicates that the so-called noise invariance in the IC is dependent on the input noisy conditions. This could suggest different preprocessing or stimulation approaches for auditory midbrain implants depending on the noisy conditions.

Computational assessment of visual coding across mouse brain areas and behavioural states

“…The results of our encoding modelling shed further light on the interaction of stimulus and behavioural parameters in modulating neuronal responses. We chose the generalised linear models (GLM), which is a common and effective encoding method in understanding and modelling neural responses (Desbordes et al, 2010;Shi et al, 2015;Hosseini et al, 2021;Williams et al, 2023), to predict the neural Frontiers in Computational Neuroscience 10 frontiersin.org activity of units in V1. Our results showed that visual stimuli contribute significantly to predicting neural activity.…”