IM2 Lab

PI: Prof. Christopher L. Buckley

School of Engineering & Informatics

Our research primarily revolves around the principles of control and optimisation in neural processing, exploring how these concepts are manifested in both biological and artificial systems. In particular, we study variational Bayesian methods in the context of control problems. For example, theories such as active inference can provide principled accounts of adaptive behaviour in terms of probabilistic inference.

Central to our work is the investigation of local learning processes, seeking to bridge the gap between theoretical neuroscience and practical machine learning applications. We focus on developing computational models that encapsulate these dynamics, aiming to provide deeper insights into both the neural basis of learning and the development of more efficient artificial intelligence algorithms.