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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

New preprint: Michael Herzog thinks that there are no mind-independent objects. In a sense, our minds create the world we perceive. We had a kind of adversarial collaboration on this, and here is the result: https://psyarxiv.com/r4sf9 I wonder what everyone thinks? I'm still unsure.

For a long time I didn't get what deep net models could tell us, but then Ratan did this awesome study and I am a convert. We no longer need to chose between "word models" and computational models, we can have a thrillingly synergistic combination of the two! https://twitter.com/apurvaratan/status/1439936744913264641

Apurva Ratan Murty@apurvaratan

Its finally here! The project that lifted my spirits through 2020-21! Super excited to finally see this online @SpringerNature @NatureComms: https://rdcu.be/cx5qI Here is a thread! 1/11

Academic job applicants: don’t take ad descriptions too literally. They are internal documents crafted not only to explain hiring interests but also to appeal to deans, uni initiatives, and other constituencies. So if it’s in your field, apply.

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