Heiko H. Schütt

This paper introduces GSN, a method to disentangle signal and noise in neural responses, showing it improves dimensionality estimates in fMRI data.

This paper introduces framed RSA, a method that evaluates neural models by incorporating both geometric patterns and population-mean response preferences, improving model selection accuracy in brain-region and network identification.

The paper introduces an open-source Python toolbox for representational similarity analysis, providing tools to evaluate neural representations and models.

The paper shows that reward prediction error neurons encode rewards efficiently, aligning with principles from sensory neuroscience and reinforcement learning.