UMRAM/ASBAM Güz 2021 Seminerleri: “Towards Solving the Hard Problem of Consciousness: The Varieties of Brain Resonances and the Conscious Experiences that they Support”

Prof. Stephen Grossberg

Boston Üniversitesi

 

Tarih/Zaman: Salı, 26 Ekim, 17:30

Zoom Meeting ID: 917 6288 9212 (Passcode: 896970)

https://zoom.us/j/91762889212?pwd=bDhvaG5sQTQxQVpuZnE5MHhKRDFtQT09

 

Konuşmacı hakkında: Stephen Grossberg is Wang Professor of Cognitive and Neural Systems; Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering; and Director of the Center for Adaptive Systems at Boston University. He is a principal founder and current research leader in computational neuroscience, theoretical psychology and cognitive science, and neuromorphic technology and AI. In 1957-1958, he introduced the paradigm of using systems of nonlinear differential equations to develop models that link brain mechanisms to mental functions, including widely used equations for short-term memory (STM), or neuronal activation; medium-term memory (MTM), or activity-dependent habituation; and long-term memory (LTM), or neuronal learning. Grossberg founded key infrastructure of the field of neural networks, including the International Neural Network Society (INNS) and the journal Neural Networks, and has served on the editorial boards of 30 journals. He was General Chairman of the first IEEE International Conference on Neural Networks in 1987, and played a key role while serving as the first INNS President in organizing the INNS First Annual Meeting in 1988. His lecture series at MIT Lincoln Lab led to the national DARPA Study of Neural Networks. He is a fellow of AERA, APA, APS, IEEE, INNS, MDRS, and SEP. He has published 17 books or journal special issues, over 550 research articles, and has 7 patents. He was most recently awarded the 2015 Norman Anderson Lifetime Achievement Award of SEP, the 2017 Frank Rosenblatt computational neuroscience award of IEEE, and the 2019 Donald O. Hebb award for biological learning of INNS.