Ta-Chu Kao

When and why does motor preparation arise in recurrent neural network models of motor control?

This study investigates why motor preparation occurs in RNN models of motor control and finds that optimal input-driven control leads to multiple phases of preparation during reach sequences.

Beyond the Euclidean brain: inferring non-Euclidean latent trajectories from spike trains

The paper extends the manifold Gaussian process latent variable model to handle non-Euclidean neural data with temporal continuity and count-based noise, improving performance on synthetic and real neural recordings.

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data

The paper introduces iLQR-VAE, a control-based variational inference method for learning dynamics and inputs from neural data, showing effectiveness on synthetic and primate reaching task data.

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models

This paper introduces a scalable Bayesian Gaussian process factor analysis (bGPFA) for neural data, showing it captures preparatory dynamics and long-timescale processes in primate motor cortex.

Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model

The paper proposes a thalamo-cortical circuit model for optimal anticipatory control of motor preparation, showing feedback control enables fast movement preparation and orthogonal activity.