Alexei A. Koulakov

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

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

Grounding olfactory perception in language: Benchmarks and models for generating natural language odor descriptions

ODIEU pairs human odor texts for 10,124 molecules with olfaction-tuned Sentence-BERT evaluators; CIRANO, a frozen GPT-2 with a molecular prefix, generates descriptions approaching human agreement.

How the olfactory bulb maintains stable odor manifolds amid adaptation and representational drift

The paper identifies three forms of representational change in the olfactory bulb during odor adaptation and shows how local plasticity and network structure maintain a stable odor manifold.

Order code in the olfactory system.

The paper investigates how odor identity is represented in the mouse olfactory bulb, finding that both primacy and order coding strategies contribute to odor representation.

A quantitative framework for predicting odor intensity across molecule and mixtures

This study develops a deep learning framework to predict odor intensity from physical properties of molecules and mixtures, overcoming limitations of traditional methods.

Weight Transfer in the Reinforcement Learning Model of Songbird Acquisition

This paper presents a computational model of songbird vocal learning that combines reinforcement learning and Hebbian plasticity to simulate song acquisition and weight transfer in neural circuits.

The primacy model and the structure of olfactory space

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

Wiring logic of the early rodent olfactory system revealed by high-throughput sequencing of single neuron projections

The paper uses high-throughput sequencing to analyze neuronal projections in the mouse olfactory system and finds structured connectivity along the anterior-posterior axis.

Spatiotemporal 3D image registration for mesoscale studies of brain development.

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

Encoding innate ability through a genomic bottleneck

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.

Mosaic representations of odors in the input and output layers of the mouse olfactory bulb

The paper examines how odor features are represented in the input and output layers of the mouse olfactory bulb, finding that physical-chemical descriptors poorly predict neuronal representations.

Network cloning using DNA barcodes

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

DeepNose: Using artificial neural networks to represent the space of odorants

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.

Computational algorithms and neural circuitry for compressed sensing in the mammalian main olfactory bulb

The paper proposes a compressed sensing algorithm in the mammalian main olfactory bulb using computational modeling and electrophysiological data, showing it can efficiently represent odors and adapt to odor mixtures.

A computational framework for converting high-throughput DNA sequencing data into neural circuit connectivity.

The paper describes a computational framework to convert high-throughput DNA sequencing data into neural circuit connectivity using BOINC and applies it to pseudorabies virus experiments.

Concentration invariant odor coding

The paper identifies a concentration-invariant odor coding mechanism based on early receptor activation and demonstrates its role in odor identification through behavioral and neural experiments.