Mackenzie Weygandt Mathis

This paper develops an interpretable AI framework to map brain dynamics onto a latent space, identifying a cerebello-prefrontal signature associated with schizophrenia symptoms.

This study shows mice use active sensing strategies to resolve visual uncertainty in a virtual reality task, selecting informative views based on visual information availability.

This paper develops a closed-loop muscle-level model of primate grasping using imitation learning and neural recordings, revealing muscle-centric and latent-goal codes in sensorimotor cortex.

This study shows mice use predictive policies to navigate scent trails by integrating bilateral olfactory input and short-term memory.

The paper identifies neuropeptides that specify and reprogram division of labor in leafcutter ants, showing their role in behavior and transcriptomic changes.

The paper uses a mouse forelimb model to show that sensorimotor prediction errors are encoded in S1 cortex more than M1 during motor adaptation.

CellSeg3D introduces self-supervised 3D cell segmentation models for fluorescence microscopy, achieving performance comparable to supervised methods without requiring ground truth labels.

The paper identifies neural ensembles in the extended amygdala of mice that encode diverse behaviours across contexts and links these ensembles to gene expression profiles associated with psychiatric and neurodegenerative disorders.

The paper uses synthetic data to compare two tasks in proprioception modeling, finding that action recognition better explains neural tuning properties than trajectory decoding.

Keypoint-MoSeq fits a generative model that tells tracking jitter apart from real movement, cutting pose time series into unsupervised sub-second behavioural syllables in mice.

The paper uses machine learning to analyze novelty-induced behaviors and finds that dopamine in the striatum correlates with individual variability and is explained by a reinforcement learning model of threat prediction.

This study shows that the HOX clock in human axial progenitors is dynamically paced by extrinsic cues like FGF signaling, enabling controlled motor neuron subtype specification.

This paper introduces a real-time, low-latency pose estimation tool with feedback capabilities for behavioral neuroscience experiments.

The paper updates and provides a step-by-step guide for using DeepLabCut, an open-source toolbox for markerless 3D pose estimation across species and behaviors.