Sander Wessel Keemink

The paper measures illusion strength in deep neural networks using the scintillating grid illusion and finds no strong correlation between illusion strength, Brain-Score, or ImageNet performance.

This paper presents a spiking neural network model that combines sampling-based inference with attractor dynamics to study head-direction systems, making testable predictions about neural activity patterns.

The paper evaluates biases in population codes when few neurons are active, showing how biases depend on noise and tuning curves and introduces a method to estimate them.

This study uses kernel-based analysis to show context-dependent deterministic processes and consistent noise in steering behavior across species and tasks.

The paper identifies biases in estimating multiple stimuli from neural population codes and shows how Gaussian Processes can model these biases.