Vijay Singh

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.

Equivalent noise characterization of human lightness constancy

The paper measures how background reflectance variation affects lightness discrimination thresholds and finds that external noise has a modest effect compared to internal noise.

What the odor is not: Estimation by elimination

The paper introduces a decoding method for olfactory systems by focusing on non-responsive receptors to identify what an odor is not, improving accuracy in determining odorants and their concentrations.

A competitive binding model predicts nonlinear responses of olfactory receptors to complex mixtures

The paper develops a competitive binding model to predict olfactory receptor responses to complex odorant mixtures, achieving 15% accuracy using single-odorant responses.

Computational Luminance Constancy from Naturalistic Images

The paper investigates how scene variability affects the estimation of object luminance reflectance using supervised statistical learning on simulated cone photoreceptor responses from naturalistic images.