Niru Maheswaranathan

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.

The paper presents a mechanistically interpretable model of retinal ganglion cell responses to natural scenes, incorporating slow adaptive dynamics and capturing retinal computations.

This paper proposes a meta-learning approach to design biologically plausible synaptic update rules for semi-supervised learning, showing effectiveness on classification tasks.

This paper introduces a time warping framework to uncover precise spike-time patterns in neural recordings across different systems, revealing both expected and unexpected firing dynamics.

The paper develops a computational framework to infer hidden structure in multilayered neural circuits, demonstrating improved prediction of retinal ganglion cell spikes using nonlinear models.