Fotios Drakopoulos

Optimal hearing aid design through restoration of the neural code

This paper develops a deep learning-based hearing aid that restores neural coding by simulating normal auditory processing in impaired brains, outperforming existing methods in distortion correction and phoneme recognition.

Modeling neural coding in the auditory midbrain with high resolution and accuracy

The paper introduces ICNet, a deep learning model that accurately simulates neural dynamics in the auditory midbrain across various sounds, including speech.

Distorted neural signal dynamics create hypersensitivity to background noise after hearing loss

The paper investigates how hearing loss distorts neural signal dynamics, leading to hypersensitivity to background noise, using brain recordings and deep neural networks.

A convolutional neural-network framework for modelling auditory sensory cells and synapses

The paper develops a DNN framework to model auditory sensory cells and synapses, improving simulation speed by 70-280 times.