The dynamics of information representation during working memory - WCUB, ENWW

Our senses capture information about the outside world, certain brain areas then transform this information and again serve it to different brain areas generating behavior. Behavior can be explained by failure or success at each of this processing stages. My thesis focuses on the second step, specifically, how and where the brain transforms information into a format that is 'readable' for brain areas that generate decisions, leading to observable behavior. I will address this problem in the frame of working memory, an active form of short term memory. Typically, a working memory task entails remembering certain objects and rules over short timescales (seconds) and then make a decision about a related, new object according to the current rule. Using machine learning algorithms, information about task variables such as color, location or rule can be decoded from the neuronal activity in certain brain regions, specifically the prefrontal cortex, while the human or animal is doing a working memory task. This allows us to track changes in the representation of relevant information i.e. observing the transformation of information leading to correct or erroneous behavior. I will analyze electrophysiological recordings from of non-human primates to investigate these questions on the level of single or small populations of neurons. Furthermore, I will collect and analyze intracranial electro-encephalography recordings from epilepsy patients bridging the translational gap between animal and human experiments. This will be complemented by a magneto-encephalography study with
healthy human subjects. Together, I hope this data will give answers on how the representation of information changes across brain regions and how this relates to behavior.

BBSRC priority and proposed research addresses
Brain Science and Mental Health
Systems approaches to the biosciences