Approaches for analyzing massively parallel neuronal data – Current and future

This session will present different approaches to analyze massively parallel neuronal data on the single neuron level in behaving animals during complex behavior. Several decades of research have clearly demonstrated that neurons do not act in isolation but achieve dynamic behavioral control in large networks that depend on neuronal interaction. To detect this interaction in living brains, large numbers of neurons must be recorded simultaneously to understand, e.g. how sensory-motor loops and behavioral control are organized at the level of populations of neurons.