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György Buzsáki

Session:
Closing lecture


Institute:
Buzsáki Lab, The Neuroscience Institute, New York University, NY

 
Website:
http://www.buzsakilab.com/

Biography:
György Buzsáki is Biggs Professor of Neuroscience at New York University. He received his M.D. in 1974 from the University of Pécs in Hungary, then earned his Ph.D. in Neuroscience in 1984 from the Academy of Sciences in Budapest. Buzsáki’s primary interests are mechanisms of memory, sleep and associated diseases. His main focus is “neural syntax”, i.e., how segmentation of neural information is organized by the numerous brain rhythms to support cognitive functions. He pioneered the experimental exploration of how coordinated, rhythmic neuronal activity serves physiological functions in the cerebral cortex. His most influential work, the two-stage model of memory trace consolidation, demonstrates how the neocortex-mediated information during learning transiently modifies hippocampal networks, followed by reactivation and consolidation of these memory traces during sharp wave-ripple patterns of sleep. To achieve these goals he has introduced numerous technical innovations from using silicon chips to record brain activity to NeuroGrid, an organic, comformable electrode system used in both animal and patients. Buzsáki is among the top 1% most-cited neuroscientists, member of the National Academy of Sciences USA, Fellow of the American Association for the Advancement of Science and the Academiae Europaeae and an external member of the Hungarian Academy of Sciences, and he sits on the editorial boards of several leading neuroscience journals, including Science and Neuron, honoris causa at Université Aix-Marseille, France and University of Kaposvar, Hungary. He is a co-recipient of the 2011 Brain Prize (with Peter Somogyi and Tamas Freund).


Abstract:

Emergence and mechanisms of cognition

The fundamental goal of the brain is to predict the future. More complex brains evolved multiple hierarchical loops between their outputs and inputs to make prediction more reliable in more complex environments and at longer time scales. With extensive training these prediction mechanisms have become ‘internalized’. At the center of this model are self-propagating loops of neuronal coalitions connected by modifiable synapses that can be propelled forward without external cues.  The implication of this conjecture is that brain networks are endowed with internal mechanisms that can generate a perpetually changing neuronal activity even in the absence of environmental inputs. I will discuss examples and mechanisms of this framework.