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).
Newsfeed 
I am incredibly excited about the new eLife feature #ScientistAndParent to which my family and I contributed.
https://elifesciences.org/interviews/24427a8f/tim-kietzmannChristmas is coming early this year: the @nvidia GPU grant program has gifted us with a brand new titan xp!
https://developer.nvidia.com/academic_gpu_seedingTwitter Feed
3.6% error on CIFAR-10 with layer-wise training and a similarity matching loss, and 7.8% error completely without back-prop. Check out my paper written together with @larseidnes https://t.co/bZI2MkoXl1
Twitter-sized history of neuroscience (biased by my interests).
"Every time we build algorithms, we curate our data, we define success, we embed our values into algorithms. Algorithms don't make things objective. Algorithms make things work for the builders of the algorithms." O'Neil
Whoa: "Our findings demonstrate a tradeoff [in the Neural Code] between robustness and efficiency across species and regions."
A Tradeoff in the Neural Code across Regions and Species https://t.co/80GhR16rMq
A very interesting article, especially this graph... https://t.co/mwgpFT9uiN
