Transient oscillations in the brain during decision making Neural Data Science


Neural oscillations are a key feature of brain activity and have been studied extensively in the context of cognitive functions and sensorimotor processing. However, recent studies have highlighted that oscillations in the brain are often transient in nature, consisting only of a few oscillation cycles (Fig. 1), rather than being sustained throughout performing a cognitive task [1-3]. Such transient oscillations have been observed in a variety of oscillation frequencies, including the theta, beta, and gamma bands, as well as in different cortical and subcortical brain regions for a range of cognitive tasks and species. In this project you will analyse oscillations recorded in the local field potential of mice performing a decision-making task (using open neural data from the International Brain Laboratory [4]). This big data set offers a unique opportunity to study oscillations in all brain regions. You will characterize the transient nature of these oscillations using e.g. lagged coherence analysis [2,5] or Hidden Markov models. Furthermore, you will examine how these oscillations relate to cognitive and motor processes in the decision-making task. The results of this project are important to better understand how transient oscillations contribute to information processing in the brain and affect behaviour.

Prerequisites: Python programming, completed courses related to neuroscience


[1] Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R. (2019). Beta oscillations in working memory, executive control of movement and thought, and sensorimotor function. Journal of Neuroscience, 39(42), 8231-8238.
[2] Muralidharan, V., Aron, A. R., Cohen, M. X., & Schmidt, R. (2023). Two modes of midfrontal theta suggest a role in conflict and error processing. NeuroImage, 273, 120107.
[3] Muralidharan, V., Aron, A. R., & Schmidt, R. (2022). Transient beta modulates decision thresholds during human action-stopping. Neuroimage, 254, 119145.

[5] Fransen, A. M., van Ede, F., & Maris, E. (2015). Identifying neuronal oscillations using rhythmicity. Neuroimage, 118, 256-267.

example illustration for a transient oscillationFig. 1: Example illustration of transient oscillations in the brain (image courtesy by V. Muralidharan)

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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