Biology subjects

Samoilova, K.

Publications and source records attributed to Samoilova, K..

4 recordsLinked to original sources

bioRxiv2026

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

Recent advances in deep learning have enabled prediction of odorant perception from molecular structure, opening new avenues for odor classification. However, most existing models are limited to predicting percepts from fixed vocabularies and fail to capture the full richness of olfactory experience. Progress is further limited by the scarcity of large-scale olfactory datasets and the lack of standardized metrics for evaluating free-form natural-language odor descriptions. To address these challenges, we introduce Odor Description and Inference Evaluation Understudy (ODIEU), a benchmark which includes perceptual descriptions of over 10,000 molecules paired with a model-based metric for evaluating free-form odor text descriptions. The model-based metric uses Sentence-BERT (SBERT) models which are fine-tuned on olfactory descriptions to allow better evaluation of human-generated odor texts. Using the fine-tuned SBERT models, we show that free-form text odor descriptions contain additional perceptual information in their syntactic structure compared to semantic labels. We further introduce CIRANO (Chemical Information Recognition and Annotation Network for Odors), a transformer-based model that generates free-form odor descriptions directly from molecular structure, thus implementing the molecular structure-to-text (S2T) prediction. CIRANO achieves performance comparable to humans. Finally, we generate human-like descriptions from mouse olfactory bulb neural data using an invertible SBERT model, yielding neural-to-text (N2T) predictions highly aligned with human descriptions. Together, CIRANO and ODIEU establish a standardized framework for generating natural language olfactory descriptions and evaluating their alignment with human perception. Code is available at https://github.com/KoulakovLab/ODIEU

animal behavior and cognition↗

bioRxiv2025

Order code in the olfactory system.

The ability to recognize odor identity across a wide range of concentrations is essential for natural olfactory behaviors. However, how odor identity is represented in the early olfactory system remains an open question. One theory proposes that glomeruli in the olfactory bulb, along with their associated odorant receptors (ORs), are activated by odorants in a temporal order that conveys information about odor identity. This order code is relatively robust to concentration changes, as the rank order of glomerular activation by a given odorant remains similar across concentrations. Alternatively, the primacy coding theory suggests that odor identity is defined by the most sensitive ORs, those that respond first, collectively referred to as the primacy set. To test these theories, we measured glomerular responses to a large set of odorants in the mouse olfactory bulb using calcium imaging. We found that receptor affinities can be embedded in a low-dimensional space (D = 10) with minimal loss of information. Within this space, we identified two clusters of glomeruli with distinct tuning properties that form independent odor representations. These clusters may correspond to the two phylogenetic classes of ORs, as revealed by both their functional characteristics and anatomical locations. In the OR affinity space, odorants evoke orderly activation waves whose directions can be used to define odor identities in the order coding model. Finally, we compared the order code and primacy model in predicting odor identity both across concentrations and across animals. Despite containing less information overall, the primacy model performed comparably to the order code. We confirmed the primacy models prediction that each receptor tends to contribute to the primacy set of at least one odorant. Analysis of binary odor mixtures revealed that mixture responses lie along geodesic lines connecting their component odors. Together, these findings suggest that odor information in the olfactory bulb may be represented through two complementary coding strategies: the primacy and order codes.

neuroscience↗

bioRxiv2025

A quantitative framework for predicting odor intensity across molecule and mixtures

In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors.

neuroscience↗

bioRxiv2017

EGL-19 AND EXP-2 ION CHANNELS MODELLING WITHIN C. ELEGANS PHARYNGEAL MUSCLE CELL ALLOWS REPRODUCTION OF CA2+ DRIVEN ACTION POTENTIALS, BOTH SINGLE EVENTS AND TRAINS

One of the current problems at the interface between neuroscience, biophysics, and computational modeling is the reverse-engineering and reproduction of Caenorhabditis elegans using computer simulation. The aim of our research was to develop the computational models and techniques for solving this problem while participating in the international open science OpenWorm Project. We have suggested models of a typical C. elegans neuron and a pharyngeal muscle cell, which were constructed and optimized using the NEURON simulation environment. The available experimental data about EGL-19 and EXP-2 ion channels allowed the model of a muscle to reproduce the action potential time profile correctly. Also, the model of a neuron reproduces quite accurately the mechanism of neural signal transmission based on passive propagation. We believe our models to be promising for better representing the specifics of various nervous and muscular cell classes when adding the corresponding ion channel models. Moreover, they can be used to construct the networks of such elements.

neuroscience↗