Ilya M. Nemenman

The paper analyzes the role of hormones in glucose regulation, showing that hormonal control overcomes limitations of local control strategies and highlights implications for artificial pancreas design.

The paper develops a mathematical model to explain ketosis-prone diabetes by proposing a reversible glucotoxicity process that accounts for remission patterns in patients.

The paper investigates host-microbe dynamics in C. elegans by combining experiments and modeling, finding that stochastic switching between growth rate phenotypes explains observed bacterial load variation better than traditional models.

The paper analyzes microbial abundance data in C. elegans to assess host-bacteria and bacteria-bacteria interactions, finding that neither model alone fully explains the observed correlations.

This study shows that millisecond-scale spike timing codes for vocal acoustics exist throughout songbird vocal development, contradicting the hypothesis that rate-based coding transitions to timing-based coding during learning.

This paper uses an efficient coding model to show that optimal E:I ratios in sensory cortices arise from trade-offs between computational accuracy and representation capacity under brain volume constraints.

This paper introduces a geometrically-embedded neural network model with random connectivity that achieves spatial memory through attractor dynamics without requiring structured synapses.

The paper shows that randomly connected neural networks can produce selectivity and decoding properties similar to real neural populations, suggesting emergent properties from random connectivity.

The paper improves a mutual information estimator by expanding its applicability and adding methods for bias verification, variance estimation, and parameter selection, tested on synthetic and biological data.

This study infers an interpretable model of C. elegans escape behavior using dynamical inference from experimental data, making accurate predictions about the behavior's dynamics.

This paper reviews the field of reverse-engineering biological networks, discussing methodologies, applications, and the justification for large-scale computational approaches in systems biology.