Iman A. Wahle

This paper introduces a novel unsupervised method, Causal Feature Learning (CFL), to discover lesion-behavior maps by learning the appropriate level of analysis and the relation between brain lesions and cognitive outcomes.

This study shows that human aesthetic preferences for art can be predicted by integrating hierarchically structured visual features in the brain across individuals.

The paper identifies direction-reversing neurons in the mouse visual system that change their preferred motion direction under different spatiotemporal stimuli, supported by an asymmetric filter model.