The Neural Algorithms Research Group (NARG) is a multidisciplinary group, integrating ideas from computational neuroscience, control engineering, and robotics. Their research is primarily focused on the cerebellar algorithm.

The cerebellum is associated with the learning and execution of skilled movements, it is essential for fine-tuning human performance in a diverse range of sensory and motor tasks. Learned motor skills are lost after cerebellar damage, and voluntary movements are greatly impoverished with large errors in the force and timing of muscle activity.

The cerebellum is divided into thousands of modules, each with identical micro-circuitry, but distinguished by unique connections with other parts of the brain. Understanding the information-processing algorithm embodied in the cerebellar microcircuit is an important step towards unravelling the nature of biological computation in adaptive control. Furthermore, the fact that cerebellum is able to automatically calibrate and adapt to changes in a wide variety of systems using a relatively flat, homogenous structure suggests that these algorithms may be highly suited to adaptive control of complex, engineered systems.

In summary, NARG’s research aims to understand the information processing algorithm embodied in the cerebellum, and also to exploit this algorithm for robotic applications.

Sheffield Robotics People:
Paul Dean
Sean Anderson
John Porrill
Christian Rössert
Emma Wilson