Tim Christian Kietzmann

The paper investigates whether neural responses to saccades and blinks are similar by analyzing EEG and eye-tracking data from real-world visual exploration.

This study shows that predictive remapping and allocentric coding in active vision emerge from energy efficiency optimization in recurrent neural networks.

The paper investigates prediction errors in visual cortex using fMRI and deep learning metrics, finding that high-level predictions influence early visual areas.

The paper shows that neural responses during natural vision are better explained by saccade-related events than by fixation onsets, challenging traditional stimulus onset paradigms.

The paper shows that visuo-semantic features explain more variance in higher-level ventral stream neural dynamics than deep neural networks, suggesting complementary roles in object recognition.

The paper identifies and models the neural and behavioural correlates of scene categorization in humans using EEG data and a recurrent convolutional neural network.

This paper shows that predictive coding in neural networks arises from energy efficiency rather than requiring hard-wired hierarchical connectivity.

This study shows cortical thickness is more linked to aging, while surface area is more linked to cognitive abilities, using brain imaging data from healthy adults.

The paper compares nine DNN architectures to explain human inferior temporal cortex representational geometry, finding trained models outperform untrained ones and all architectures achieve similar performance after training and fitting.

This study uses a naturalistic free viewing paradigm to reveal new EEG signatures of face processing with reduced subject variability compared to traditional methods.

This study shows that small changes in initial conditions of deep neural networks lead to significant differences in their representations, even when classification performance is similar.

This paper shows recurrent neural networks outperform feedforward models in visual recognition tasks, enabling flexible speed-accuracy trade-offs and better prediction of human reaction times.

This paper identifies stable developmental clusters of subcortical volumetric change across the lifespan and shows genetic organization within these clusters.

The paper examines the relationship between self-reported sleep parameters and cortical thinning in aging, finding associations with specific brain regions but not with memory decline or amyloid-β accumulation.

The paper examines how self-reported sleep relates to hippocampal atrophy across adulthood using data from the Lifebrain consortium and UK Biobank, finding that worse sleep is linked to faster hippocampal volume loss over time.

This paper discusses how deep neural networks can model neural responses and cognitive functions, highlighting their accuracy and potential in computational neuroscience.