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Nikos Logothetis: “Multidisciplinary Approaches for the Study of Self-Organization Principles in the Primate Brain”
15:30 to 16:30 - 26th February 2020
Faculty Club (16.6)

Max Planck Institute for Biological Cybernetics (MPI-BC) Tübingen

Director of Department of Physiology of Cognitive Processes

If you do not know Nikos Logothetis you must come. If you already know him, you will come, because he is very broad-minded, an excellent lecturer, and an absolute leading systems neuroscientist.

Abstract
States of an organism, such as sleep, wakefulness, aphasia or attention have been intensively studied for years, but our understanding of the actual dynamic-condition of brain-networks related to them remains poor, despite of all developments in systems neuroscience. For that matter there is hardly any quantitative definition of a “brain-state”, let alone of its transitions and evolution reflecting both the underlying anatomy and the complexity if its dynamics. This should be hardly surprising, as the primate brains - in particular - have billions of neurons and trillions of synapses, nested recursive circuits at all possible spatiotemporal levels, massive connectivity, and initial condition dependent activity-evolution; all typical characteristics of so-called complex dynamic systems. Such systems can only be adequately investigated by using multimodal and multiscale methodologies, in studies combining theoretical and experimental approaches. An example is the combination of concurrent electrophysiological recordings of local, intrinsic neural events and imaging of brain-wide activity patterns associated with them. In my talk, I shall briefly describe our observations related to perception and recognition in non-human-primates, and its striking similarity with that of humans, introduce our multidisciplinary methodologies for assessing brain-states, and give an example thalamic, hippocampal and pontine neural events, their interrelationship of which, together with the fMRI of multi-structure activity patterns, offer some insights into the self-organization of networks, potentially related to learning and memory.

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