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Welcome to the page of Dr. Tim Christian Kietzmann. I am an Assistant Professor at the Donders Institute for Brain, Cognition and Behaviour (Radboud University), and a Research Associate at the MRC Cognition and Brain Science Unit (University of Cambridge). I 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. Feel free to contact me with any questions or paper requests, and follow me on twitter (@TimKietzmann) for latest updates.




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 neuroimaging data, 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 field of cognitive computational neuroscience.

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Twitter Feed

An analysis of more than 70,000 journal articles shows that papers with a #preprint received 49% more attention and 36% more citations than articles without one https://t.co/qM5xZmUiFG #MetaResearch

New @biorxivpreprint from the lab (https://t.co/ZY8PlZBUn1) that shows choice-predictive feedforward/back signals in V1! Led by @Nwilming. Relevant for anyone interested in #decisionmaking, #evidenceAccumulation, #brainrhythms, #corticalfeedback. Thread. (1/10)

Performance artist generates virtual traffic jams in Google Maps by pulling a wagon full of smartphones
https://t.co/ZOICiqYWKW

Interested in decision-making, evidence accumulation, normative or circuit models, oscillations, cortical hierarchy, feedback, or brainstem arousal systems? Check out our new paper on @biorxivpreprint (https://t.co/7BvEWOj9u9)! Led by @neuromurphy, summary below. (1/11)

A little blog post on noise ceilings. If you've ever wondered about the relation between the noise ceilings you come across in the encoding model literature and the ones used in RSA, this might help: https://t.co/ZKG7Zk8pBK

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