C. Libedinsky

Prefrontal Manifold Geometry Explains Reaction Time Variability

This study uses a dynamical systems approach to explain reaction time variability by analyzing primate frontal eye field activity and microstimulation, proposing that manifold geometry amplifies noise during movement preparation.

Lightweight Reinforcement Algorithms for autonomous, scalable intra-cortical Brain Machine Interfaces

This paper introduces Banditron, a lightweight RL algorithm for iBMIs, showing significant power savings and performance improvements over state-of-the-art methods.

Towards Autonomous Intra-cortical Brain Machine Interfaces: Applying Bandit Algorithms for Online Reinforcement Learning

This paper applies Banditron, an online reinforcement learning algorithm, to improve decoding performance in intra-cortical brain machine interfaces using non-human primate data.

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

This paper introduces a sparse ensemble machine learning method to improve decoding robustness in iBMIs by reducing sensitivity to recording channel variations, achieving up to 24% accuracy improvement in non-human primates.

Towards Intelligent Intra-cortical BMI (i2BMI):Low-power Neuromorphic Decoders that outperform Kalman Filters

This paper introduces a low-power neuromorphic decoder, SELMA, which outperforms Kalman filters in continuous state decoding for intra-cortical BMI systems.