Hidenori Tanaka

The paper introduces latent processing units (LPUs) to explain how neural networks perform reliable computations despite variability, using six theorems to unify geometric and dynamical perspectives.

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 models how gene drives spread spatially and identifies conditions for controlled release and containment.