Stephen I. Ryu

Signatures of covert neuron loss in the local field potential of motor cortex

This paper investigates the effects of covert neuron loss on local field potential spectra in motor cortex using electrolytic lesions in large animals, finding prolonged spectral changes despite no behavioral impact.

Secreted phospholipase A2α generates a pathogen-derived lysophospholipid to signal local immunity

The paper identifies lysophosphatidylethanolamine (LPE18:1) as an immune signal generated by host PLA2 enzyme from pathogen lipids, linking NLR activation to localized defense responses.

Learning alters neural activity to simultaneously support memory and action

The paper shows that learning a new BCI map alters neural activity in a way that preserves performance on a familiar map, suggesting a mechanism for learning multiple motor behaviors without interference.

Skill-specific changes in cortical preparatory activity during motor learning

This study investigates how motor learning alters preparatory neural activity in monkeys, finding both task-specific and uniform shifts that may support motor memory retention.

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

This paper demonstrates an optical brain-computer interface (oBCI) using dendritic calcium signals from rhesus macaques during motor tasks, achieving stable decoding of movement direction.

Learning is shaped by abrupt changes in neural engagement

The paper investigates how internal states like arousal affect neural engagement during learning in non-human primates using a BCI paradigm, finding that abrupt changes in engagement influence learning speed for different task goals.

Decoding and perturbing decision states in real time

The paper decodes real-time decision variables from motor cortex activity and shows their link to choice behavior and decision dynamics.

Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping

This paper introduces a time warping framework to uncover precise spike-time patterns in neural recordings across different systems, revealing both expected and unexpected firing dynamics.

Accurate estimation of neural population dynamics without spike sorting

The paper shows that neural dynamics can be accurately estimated without spike sorting by using multi-unit threshold crossings, enabling new analyses and electrode array design.

Inferring single-trial neural population dynamics using sequential auto-encoders

This paper introduces LFADS, a deep learning method to infer single-trial neural population dynamics from spiking data, demonstrating improved accuracy in predicting behavior and extracting dynamics across datasets.

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

This paper introduces tensor components analysis to uncover low-dimensional neural dynamics across multiple timescales in diverse datasets.

Population dynamics of choice representation in dorsal premotor and primary motor cortex

This study examines how decision variables are represented in neural populations of PMd and M1 during a motor task, showing differences in timing and strength between areas and replicating findings with a computational model.

A theory of multineuronal dimensionality, dynamics and measurement

Task complexity sets how many dimensions multineuronal activity occupies and when dynamical portraits recovered from a tiny fraction of neurons can be trusted; tested on monkey reaching recordings.