Publications by Mackenzie Mathis

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Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to chara ...

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions ...

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into ...

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their ...

Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and develope ...

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external ...

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

Recent work is revealing neural correlates of a leading theory of motor control. By linking an elegant series of behavioral experiments with neural inactivation in macaques with computational models, a new study shows that premotor and parietal areas can b ...

Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging. Being able to study this incredible agility will be critical for the development of n ...

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because key ...

Social insects offer powerful models to investigate mechanisms of elaborate individual behaviors comprising a cooperative community. Workers of the leafcutter ant genus Atta are extreme examples of behavioral segregation among phenotypically distinct worke ...

We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize ac ...

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental w ...

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

############# Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023 ############# Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander ...

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes ...

Mackenzie Mathis

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Contrasting action and posture coding with hierarchical deep neural network models of proprioception

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Learnable latent embeddings for joint behavioural and neural analysis

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Ethological computational psychiatry: Challenges and opportunities

SuperAnimal pretrained pose estimation models for behavioral analysis

Perspectives in machine learning for wildlife conservation

Decoding the brain: From neural representations to mechanistic models

NeuroAI: If grid cells are the answer, is path integration the question?

Real-time, low-latency closed-loop feedback using markerless posture tracking

Pretraining boosts out-of-domain robustness for pose estimation

Motor control: Neural correlates of optimal feedback control theory

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Neuropeptides specify and reprogram division of labor in the leafcutter ant Atta cephalotes

Next-generation brain observatories

Measuring and modeling the motor system with machine learning

Brain dynamics uncovered using a machine-learning algorithm

Data for Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings