Richard Gao

This paper introduces a cell-based sensor called LUNAR that detects extracellular double-stranded DNA with nanomolar sensitivity in mammalian cells.

The paper introduces a VAE-based method to model conditional distributions between neural and behavioral data, validated on fly and monkey datasets.

This study evaluates and compares different measures of aperiodic neural activity, showing shared variance across methods and differences in their sensitivity to oscillatory activity.

This paper uses denoising diffusion probabilistic models to generate realistic neurophysiological time series, capturing both general and dataset-specific features across multiple species and recording modalities.

The paper introduces AutoMIND, a method for inverse modeling of neural circuits, revealing dynamic-dependent invariances in parameter configurations across different neural dynamics.

This paper introduces simulation-based Bayesian inference to efficiently identify generative models in connectomics by inferring wiring rule parameters from empirical data.

This paper introduces MNLE, a simulation-based inference method for decision-making models, showing it is more efficient and accurate than existing methods.

An open-source Python algorithm decomposes neural power spectra into a dynamic aperiodic 1/f background and Gaussian oscillatory peaks without requiring predefined canonical frequency bands.

The paper infers neuronal timescales from human intracranial recordings and shows they align with cortical microarchitecture and cognitive functions.

This paper shows that cortical organoids model early human brain network development by exhibiting oscillatory activity that transitions to patterns resembling preterm EEG.

The paper shows that neural oscillation power is not normally distributed and introduces methods to reduce overestimation caused by high-power bursts.

This paper develops a computational model to estimate the excitation/inhibition ratio from electrophysiological power spectra, validated across two species.