I. Nemenman

Maximum Likelihood Estimators For Colony Forming Units

The paper develops methods to estimate Colony Forming Units (CFUs) by linking serial dilution techniques and discusses error bounds and assumptions for various analytical approaches.

Multi-dimensional structure of C. elegans thermal learning

The paper develops a multi-modal model to explain C. elegans thermal preference dynamics, identifying two independent pathways influenced by temperature and food.

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

The paper shows that latent dynamical variables can produce spatiotemporal criticality signatures in neural populations, suggesting that critical behavior arises from coupling to hidden stimuli.

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

This paper introduces a Bayesian Ising method to detect spike patterns in songbird neural data, revealing how specific motor behaviors are encoded.

Physical limit to concentration sensing in a changing environment

The paper derives a new physical bound on concentration sensing accuracy in fluctuating environments and shows how it can be achieved with a biochemical network.

Receptor crosstalk improves concentration sensing of multiple ligands

This study shows that receptor crosstalk can enhance precision in sensing multiple ligand concentrations by utilizing non-cognate ligand binding events.

Increased adaptability to rapid environmental change can more than make up for the two-fold cost of males

The paper shows that increased adaptability from anisogamy can offset the two-fold cost of males by allowing faster adaptation through super-successful males in changing environments.

Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds

The paper develops a non-Gaussian Bayesian theory of vocal learning in songbirds that explains behavioral observations through distributional learning and confirms predictions with experimental data.