Home

Home

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 rss
Twitter Feed

Oh! @nschawor was hiding on twitter! Say hello, everyone.

https://t.co/ItpvWWp4Z7

(4/5) In a related work with @GMvandeVen, we found that a brain-inspired solution – replaying representations representative of previous experiences generated by the network’s own feedback or backward connections – performs well on all three scenarios. https://t.co/zjzzxNODZL

(2/5) Comparing proposed solutions isn’t easy though, as everyone uses different rules for evaluating performance. To enable better comparisons, in our new preprint with @GMvandeVen we describe three different “scenarios” for continual learning. https://t.co/WUqiWCGqK4

Simulating Item-Effects: If you use few, here 5 stimuli per dataset & there is stimulus variability, e.g. different difficulties/stim & you do not account for it, e.g. by a mixed model, then you might find false positive effects!

(one frame is a random sample of stimuli)

Check out our new @biorxiv_neursci preprint by @nadine_dijkstra et al on the influence of imagery and sensory input on conscious perception!
https://t.co/3ykLJPNTMn https://t.co/xKnmmvBMFd

Load More...