Skip to main content

Left/Right brain, human motor control and implications for robotics

Primary supervisor

Levin Kuhlmann


  • Gideon Kowadlo

The brains of all bilaterally symmetric animals on Earth are divided into left and right hemispheres. The anatomy and functionality of the hemispheres have a large degree of overlap, but they specialize to possess different attributes. This principle is poorly understood and has not been exploited in AI/ML. Previously, we mimicked biological differences between hemispheres, and achieved specialization and superior performance in a classification task that matched behavioral observations. Similar mechanisms are likely to underpin specialization observed in motor control, where one side specializes in the control of trajectories and the other in the control of posture. This project investigates that question by building a model with left and right neural networks to perform a motor task, and compare to human performance, and standard ML approaches. This will help with one of the biggest mysteries in cognitive science, ‘why are brains divided into left and right?’, and constitutes a new principle in AI/ML.



Student cohort

Double Semester

Required knowledge

Machine Learning, Deep Learning or some knowledge and willingness to learn. Must have Python and some experience with PyTorch or Tensorflow.