Christopher Versteeg

This paper introduces DynISys, a model that infers neural dynamics and communication from multi-area recordings, revealing mechanisms for motor planning and execution.

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

The paper uses task-driven neural networks to predict neural activity in proprioceptive pathways during reaching tasks, finding that task-optimized models outperform other models.

The paper was withdrawn due to an error in the data processing pipeline, and the authors do not wish it to be cited.

The paper examines proprioceptive neuron activity in the cuneate nucleus of awake monkeys during active and passive limb movements, finding directional tuning and differential response amplification.

This study compares GLMs to machine learning methods for predicting neural spikes and finds that modern methods like XGBoost and ensembles are more accurate and robust.