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Representational dynamics - How can we understand the temporal evolution of distributed brain activity patterns?

Abstract

Brain information processing is inherently multivariate and highly dynamic. Perception, cognition, and motor control all rely on rapid recurrent computations, with representations emerging, transmuting, and waning according to the brain's own rhythm as information flows continually and bidirectionally between interacting areas. The field is increasingly acquiring multichannel measurements with high temporal resolution in humans (using MEG, EEG and ECog) and animals (using multi-electrode recordings and ECog). Advances in technologies for measuring brain activity will further increase the spatial and temporal resolution at which we can observe brain activity. The challenge is how to make sense of the detailed signatures of brain information processing that lie latent in such data. A number of studies have engaged the complexity of spatiotemporal brain-activity patterns by analysing patterns of activity as a function of time. Established methods include temporal-window pattern decoding and representational similarity analysis, as well as visualisations of dynamic representational trajectories using dimensionality reduction methods. Dynamic multivariate analysis is going to be a key element of systems neuroscience in humans and nonhuman models. However, it is unclear how to best characterise and visualise dynamic representations, what features to focus on (evoked, induced, frequency-dependent), how to analyse causal interactions and information exchange between brain areas, and how to perform inference comparing alternative models of brain information processing.

This workshop brings together leading researchers in the field for a set of highly interactive talks that communicate particular novel neuroscientific insights along with a detailed explanation of the more generally applicable analyses that enabled the insights.

Schedule