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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Typically, we think of intrinsic motivation as _maximizing_ surprise. But agents in complex worlds with unexpected events can learn meaningful behaviors by _minimizing_ surprise, leading to behaviors that seek out homeostasis: https://t.co/UykHFkRJQl

Can learn vizdoom w/o reward

Happy to announce that the paper "Computational Resource Demands of a Predictive Bayesian Brain" by @IrisVanRooij and @JohanKwisthout is online now in @CompBrainBeh and freely accessible: https://t.co/TqY7oOVNlG 1/7

Q: In terms of CO2, how much better is it to meet via Skype than in person when driving would be needed?


I did a bit of digging and came up with this approx rule of thumb:

1h of Skype with 2 people ≈ 1km driven by a car.

May be I am the only weird one who feels uncomfortable with the idea that one of the pre-readings list includes chapters from three books that are not publicly available as free versions. Perhaps @GaryMarcus can share those chapters with us? Or is it indirect books marketing? https://t.co/azAqpNtuZ6

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