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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Our new preprint, all the hard work was by Muxuan Lyu and Kyoung Whan Choe, "Scenes that produce more consistent fixation maps are more memorable" https://t.co/9MycLFkDpu

What does it mean to understand a neural network: https://t.co/Deif3SG0BY - Tim Lillicrap and me try to think deeply about how pondering neural networks changes our goals as neuroscientists.

PUBLIC SERVICE ANNOUNCEMENT: You must cross-validate sorted plots!! E.g. peak-latency sorted colormaps of neural activity. Otherwise you WILL find "sequences" out of random noise. Don't believe me? A proof in four lines. Anyone feel like you have seen the last panel before?

Got an email from a new grad student asking for recommendations for resources to better understand research design and statistical inference. Here’s what I’m going to tell them...

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