Sergey A. Shuvaev

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

The paper develops a theoretical framework to map odor input space to olfactory receptor responses, finding that Kenyon cells integrate high-affinity receptor inputs.

This paper develops a multi-step algorithm for registering perinatal mouse brain images across developmental stages, achieving accurate alignment and synchronization.

The paper explores how neural circuits can be compressed through a genomic bottleneck, showing that this compression can enhance transfer learning and suggest implications for AI design.

The paper introduces a method to clone neural networks using DNA barcodes by solving a synthesis problem with efficient computational approaches.

This paper trains an autoencoder to represent odorant chemical space and uses it to predict human olfactory percepts with accuracy comparable to traditional molecular descriptors.