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Dennis Molfese to speak at EGI's EEG School

2 July 2015 – EGI is proud to present the inaugural EGI Educator Award to Dr. Dennis Molfese, world renowned researcher in cognitive neuroscience at the University of Nebraska, Lincoln, in recognition of his important work in advancing human brain research and in educating people on the advanced methods of human brain electrophysiology. Dr. Molfese will be giving the keynote lecture, “The dynamic links between mind and brain: The next frontier” at EGI’s annual EEG School this summer in Eugene, Oregon, USA.


Although research into the neural processes involved in cognition has greatly expanded over the past several decades, progress in identifying the neural mechanisms engaged within brain areas has been largely ignored. Investigators continue to focus on the general functions carried out by a brain area or region. Usually the characterization of a brain area/region is made using broad terms such as “decision making” or “inhibition.” As a consequence, there are a number of brain areas, some located in quite disparate brain areas, that are labeled with the same adjectives and with no attempt by investigators to describe the neural-cognitive operations involved in such functions of how information is altered as it moves from one brain area to another. From a Hebbian perspective, if a cognitive process engages a network of brain areas, how do these areas contribute to a resulting cognitive event. Are the effects cumulative? Or within a neural network, are the neural signals going into one brain area different from those that emerge from that area that then are input into the next brain area that makes up part of a single multi-site network? If there are changes in the information moving from one brain area to the next within a network, does the neural signal change? If it does, how does it change over time and across areas?

At present there is no systematic theory to direct research questions or generate testable hypotheses regarding the operations of neural networks. We advance a theory that emphasizes changes in spatial and temporal distributions of the brain’s neural networks during normal learning and the disruptions of these networks following injury. Specific predictions are made regarding both the development of the network during learning its breakdown following injury, and its recovery following injury.

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