Meng Li

This paper evaluates pseudo-likelihood-based Bayesian inference for species tree estimation under the multispecies coalescent, showing theoretical guarantees and empirical performance on synthetic and biological data.

The paper introduces a theory of neural coding based on self-information principles and demonstrates its application in identifying cell-assembly patterns across various brain activities.

The paper shows spike-timing patterns across different neurons and species follow a gamma-distribution, remain stable across cognitive states, and break down under anesthesia.

The paper proposes that neuronal variability encodes information through surprisal spikes, offering a new framework for understanding neural coding and its biological implications.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.