Speakers > Tim O’Leary

Tim O'Leary

Cambridge Neuroscience, university of Cambridge, UK

Timothy O’Leary is an associate professor of computational Neuroscience at the University of Cambridge (UK). After a training in mathematics, Timothy O’Leary obtained a PhD in neuroscience, performing electrophysiological recordings of cultured neurons, focusing in particular on homeostatic plasticity of synaptic function. He then became a post-doctoral fellow in the Marder lab where he published several important theoretical studies investigating the biophysical principles underlying the robustness of neuronal activity. His work addresses in particular the paradox that exists between the variability of neuronal components and the stability of neuronal output.

Continually reconfiguring neural circuits and feedback control in the brain

In 1950, Norbert Wiener, the founder of cybernetics and a pioneer of control and information theory, asserted "We are but whirlpools in a river of ever-flowing water. We are not stuff that abides, but patterns that perpetuate themselves. A pattern is a message, and may be transmitted as a message… It is amusing as well as instructive to consider what would happen if we were to transmit the whole pattern of the human body, of the human brain with its memories and cross connections, so that a hypothetical receiving instrument could re-embody these messages in appropriate matter.” More than seventy years later, the view that the important stuff of the mind, nervous system and of life itself can be abstracted as a self-regulating system is more prescient than ever. Recent advances in neural recording technology have enabled us to identify and probe neural representations of behaviour as it happens, revealing a dynamic neural code that continually reconfigures, as in Wiener’s whirlpools. I will describe our recent attempts to identify homeostatic principles that enable the brain to retain learned information while remaining plastic. This work helps us understand and potentially interface with dynamic cortical neural representations in behaving animals, and provides a mathematical framework that unifies neural variability, feedback and neural population codes.