Master Thesis or Semester Project: Transformation of Neural representations of Vocalizations and Voices

The brain exhibits a sophisticated capacity for auditory processing, particularly when it comes to the complex sounds of vocalizations and voices. Specific regions within the temporal lobe are crucial for decoding these signals, enabling recognition of both the what (content of the vocalization) and the who (speaker identity). Understanding how neural representations of sound are transformed as they traverse this processing stream is a fundamental challenge in neuroscience. This project aims to understand how sounds are processed and transformed across the auditory processing hierarchy in the temporal lobe.

 

Project description

This project focuses on analyzing single-unit and multi-unit recordings obtained from in vivo micro-electrode arrays. The dataset includes recordings from simultaneous implantation of multiple arrays, providing a rich source of information about neural activity during voice processing. This project offers a unique opportunity to work with large-scale neural datasets and apply advanced analysis techniques.

 

Methodology

In this project, you will analyze neural data to identify how voice features are represented and transformed across the sound processing hierarchy. You will apply advanced signal processing analysis, such as Granger causality and subspace analysis to quantify the influence of one brain area on another and to characterize the information content of neuronal populations.

 

Requirements

- Proficiency in Python is essential.

- A solid foundation in mathematics (linear algebra) for understanding and implementing the analytical methods and computational models.

 

Contact

- Prof. Timothée Proix: proix@ini.ethz.ch

- Dr. Margharita Giamundo (Aix-Marseille University)

 

Starting date and duration

This project is currently available as a semester project or master thesis.

 

Related literature

- Giamundo et al. (2024). A population of neurons selective for human voice in the monkey brain. PNAS 121(25). https://doi.org/10.1073/pnas.2405588121

- Perich et al. (2018). A Neural Population Mechanism for Rapid Learning. Neuron 100(4). https://doi.org/10.1016/j.neuron.2018.09.030