Dr F. Zeldenrust (Fleur)

Associate professor - Donders Centre for Neuroscience – Neurophysics
Associate professor - Donders Institute for Brain, Cognition and Behaviour

Dr F. Zeldenrust (Fleur)
Visiting address

Heyendaalseweg 135
Internal postal code: 33

Postal address

Internal address: 33 Postbus 9010

Working days Monday, Tuesday, Wednesday morning, Thursday, Friday

With a training in both Physics and Neuroscience, I have a broad interest in quantitative solutions to all types of scientific problems, but my expertise (and PhD) is in the field of Computational Neuroscience. I have experience in programming in different environments, including MatLab, XPP, Python, Brian and Neuron. Next to my research I am very motivated about science education at different levels, from primary school students and their teachers, to secondary school students, undergraduate students and graduate students. Every level brings its own challenges and wonders. In 2019 I have joined the board of directors of the Organisation for Computational Neurosciences. In 2020, we have founded the Radboud Young Academy. In 2021, I became the speaker of the theme ‘Neural Computation and Neurotechnology’ of the Donders Institute. I recently also became a member of the Young Academy of the Royal Netherlands Academy of Arts and Sciences.

The brain continuously processes information. The physical structure of the brain (its ‘hardware’) shapes this information processing and vice versa: the computations needed for information processing (the ‘software’) are adapted to the physical structure of the hardware. In my ‘Biophysics of Neural Computation’ group, we study the relationship between the physical properties of the brain and its information processing: how are neurons and networks formed so that they can perform functions such as perception? Which characteristics of neurons and networks enhance or limit information transfer? We humans still strongly outperform machines and computers in tasks such as facial recognition or adaptation to changes in illumination. Understanding how the brain does this can help us improve the performance of such devices. We study these questions using a variety of theoretical methods, from neural network modelling to abstract coding models and advanced data analysis of experimental data, through close collaborations with experimentalists.

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