The Mathis Lab is led by Professor Mackenzie Mathis.
The goal of the laboratory is to reverse engineer the neural circuits that drive adaptive motor behavior by studying artificial and natural intelligence. We hope that by understanding the neural basis of adaptive motor control we can open new avenues in therapeutic research for neurological disease, help build better machine learning tools, and crucially, provide fundamental insights into brain function.
Here are some research aims that guide us:
- How do animals adapt to their environment over short and long timescales?
- What neural computations enable adaptive behavior?
- Can we make more biologically-inspired artificial intelligence?
- Can we use these circuit-level and machine-learning advances to restore function in neurodegeneration and neurological injury?
We believe behavior is an essential component to understanding neural function. As part of our quest to better understand behavior, we develop new tools to study more complex and natural movements. Here is some technology that we utilize, and develop, to answer those questions:
We develop computer vision tools, like DeepLabCut™, to perform markerless pose estimation and behavioral analysis from any species in a multitude of settings. For our purposes, we use these key-points to study kinematics and motor learning during a variety of skills tasks, and during freely moving natural behaviors. We also will continue to collaborate with the Mathis Group on these types of tools.
We also have developed a set of skilled motor tasks where mice can learn from a dynamically changing sensory landscape.
By combining concepts from machine learning and optimal motor control with the power of the mouse's genetics and accessibility, our lab aims to uncover fundamental principles that guide motor adaptation, learning, and motor control. Read more here.
We are using the latest techniques in 2-photon and deep brain imaging (including utilizing multi-area imaging with a 2-photon mesoscope), to uncover the neural correlates of adaptive behavior. We use optogenetics and chemogenetics to test what roles diverse areas have during behavior. Furthermore, we develop new computational models and tools to generate testable hypotheses and analyze our data.