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Welcome to the page of Dr. Tim Christian Kietzmann. I am a Researcher and Graduate Supervisor at the MRC Cognition and Brain Science Unit of the University of Cambridge (line manager Prof. Niko Kriegeskorte). 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. My main research aim is to understand dynamic information processing in the brain. Focusing mainly on vision, I am particularly interested in understanding the cortical mechanisms that allow us to robustly extract information from noisy sensory information. I ask how the brain learns robust representations from the statistical regularities in the world. What are the underlying computational mechanisms and representational transformations? What are the computational objectives that the visual system optimises for, and how do they shape neural representations? What temporal dynamics govern information processing and how does experience affect them?

I approach these questions by combining human neuroimaging with machine learning techniques (pattern recognition, and deep neural network models).

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Authors should use common words to say uncommon things. Arthur Schopenhauer, German philosopher. 1788-1860. #PhDForum #EMCRforum #acwri #SUAWUK #vitae18

If we agree that Marr's "levels of analysis" are a viable way to describe inquiry, and also agree that theorizing across levels is important, where do you find your own level of scientific curiosity to be the strongest? Where do you live most of the time? #cogsci pls R/T

The geometry of high-level concepts (context, rules, etc) in neural population

saw great talk on this yesterday, very interesting ideas on how to extract flexible states [from Silvia Bernardi, Fusi, Salzman] https://t.co/ar3myBtP6D

If you want to play around with fitting models to retinal data, we released *all* of the data and code used in this paper! Check it out at https://t.co/R2Ty1ovTCW #openscience https://t.co/oFKrXYnz2s

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