Neural circuits and Bayesian inference: the mathematical microscope
Peter Zeidman is a Senior Research Fellow at the Wellcome Centre for Human Neuroimaging at University College London. His PhD research was on high-resolution functional neuroimaging of the hippocampus, in the lab of Professor Eleanor Maguire. He now works with Professor Karl Friston, developing methods for inferring the activity of neural networks using functional neuroimaging data. Peter applies the methods he develops to investigate the neurobiology of memory and perception in healthy and disordered ageing.
Functional neuroimaging typically involves testing hypotheses about biological mechanisms that cannot be directly observed, using downstream measurements such as fMRI, EEG or MEG. This is a technically challenging “ill-posed problem”, because different configurations of neural circuits could give rise to similar data. This is resolved by using statistical methods that quantify uncertainty when making inferences and testing hypotheses. Dynamic Causal Modelling (DCM) is one such approach, which has proven useful for making probabilistic inferences about neural and vascular dynamics. In this talk I will introduce recent developments in this field, with illustrative applications to multi-modal neuroimaging in cognitive and clinical neuroscience.