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Welcome to the page of the Kietzmann Lab at the AI department of the Donders Institute for Brain, Cognition and Behaviour (Radboud University). We investigate principles of neural information processing using tools from machine learning and deep learning, applied to neuroimaging data recorded at high temporal (EEG/MEG) and spatial (fMRI) resolution. Please contact us with any questions or paper requests, and follow Tim on twitter (@TimKietzmann) for latest lab updates.

Please reach out to us if you are interested in joining the lab and see our page on equity, diversity, and inclusion for further information.


Research Interests

Cognitive Neuroscience meets Machine Learning. Our research group aims to understand the computational processes by which the brain and artificial agents can efficiently and robustly derive meaning from the world around us. We ask how the brain acquires versatile representations from the statistical regularities in the input, how sensory information is dynamically transformed in the cortical network, and which information is extracted by the brain to support higher-level cognition. To find answers to these questions, we develop and employ machine learning techniques to discover and model structure in high-dimensional neural data.

As a target modality, we focus on vision, the most dominant of our senses both neurally and perceptually. To gain insight into the intricate system that enables us to see, the group advances along two interconnected lines of research: machine learning for discovery in neuroscience, and deep neural network modelling. This interdisciplinary work combines machine learning, computational neuroscience, computer vision, and semantics. Our work is therefore at the heart of the emerging fields of neuro-inspired machine learning and cognitive computational neuroscience.

Newsfeed
Twitter Feed

Do we still need SGD/Adam to train neural networks? Based on our #NeurIPS2021 paper, we are one step closer to replacing hand-designed optimizers with a single meta-model. Our meta-model can predict parameters for almost any neural network in just one forward pass. (1/n)

MSc programs at Radboud often require students to upload a photo of themselves as part of their application.

I have no idea what the looks of a student have to do with their academic potential. The Cognitive Computing MSc AI program will therefore not follow suit.

Reminder!!!

#NAISys2022 is happening in April 2022, and the website is live:

https://meetings.cshl.edu/meetings.aspx?meet=NAISYS&year=22

If you're interested in how neuroscience can help guide AI development, this will be the meeting to attend! 🧠💻

CC @TonyZador @doristsao

Please RT!!!

I would tell young researchers if you aim for the sky you may land on a cloud but if you aim for a cloud you likely end on a rooftop. Point is don’t limit yourself much when conceiving a project. Let ideas go wild for a bit and then ask others if you went too far. Then iterate.

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