Julia H. Wang

This paper introduces a multi-modal, multi-species, and multi-task latent-space model to decode consciousness levels from neural recordings, achieving cross-species and zero-shot transfer.

The paper develops a three-layer network model to predict retinal responses to natural scenes, revealing fast dynamics in ganglion cell signaling and the role of interneurons in generating the retinal code.

This paper explores parallels between normalization strategies in artificial and biological neural networks, arguing for functional equivalence and proposing a new algorithm inspired by synaptic scaling.