Each Lecturer will hold one/two lesson(s) on a specific topic. The Lecturers below are confirmed.
Topics
Cognitive Neuroscience, Neuroscience, AIBiography
Marvin M. Chun, Dean of Yale College. Marvin M. Chun is the Richard M. Colgate Professor of Psychology with a secondary appointment in the Yale School of Medicine Department of Neuroscience. He is also a member of the Yale Cognitive Science Program.
Marvin Chun leads a cognitive neuroscience laboratory that uses brain imaging and machine learning to study how people see, attend, remember, and perform optimally. One line of work uses brain imaging to read out perceptions and thoughts. Another major project uses brain scans to understand and predict what makes people different.
Chun is the Richard M. Colgate Professor of Psychology with secondary appointments in the Yale School of Medicine Department of Neuroscience, and the Yale Cognitive Science Program. He also serves as the Dean of Yale College, the first of Asian descent in Yale’s 319-year history. Chun received his undergraduate degree from Yonsei University, his Ph.D. from the Massachusetts Institute of Technology, and postdoctoral training at Harvard University. He was a graduate student fellow of the Korea Foundation for Advanced Studies. Throughout his career, his research has been honored with several awards, such as the Troland Research Award from the United States National Academy of Sciences, the American Psychological Association Distinguished Scientific Award for an Early Career Contribution to Psychology, and the Samsung Ho-Am Prize in Science. At Yale, his teaching has been recognized with both the Phi Beta Kappa William Clyde DeVane Medal for Distinguished Scholarship and Teaching, and the Lex Hixon ’63 Prize for Teaching Excellence in the Social Sciences.
Topics
Theoretical neuroscience, Computational neuroscience, Neural coding, Population coding, MemoryBiography
Ila Fiete is a Professor in the Department of Brain and Cognitive Sciences and an Associate Member of the McGovern Institute at MIT. She obtained her undergraduate degrees in Physics and Mathematics at the University of Michigan and her M.A. and Ph.D. in Physics at Harvard, under the guidance of Sebastian Seung at MIT. Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. She was subsequently on the faculty of the University of Texas at Austin in the Center for Learning and Memory. Ila Fiete is an HHMI Faculty Scholar. She has been a CIFAR Senior Fellow, a McKnight Scholar, an ONR Young Investigator, an Alfred P. Sloan Foundation Fellow and a Searle Scholar.
https://mcgovern.mit.edu/profile/ila-fiete/
https://fietelab.mit.edu/
Topics
NeuroscienceBiography
Professor Karl J. Friston MB, BS, MA, MRCPsych, FMedSci, FRSB, FRS
Wellcome Principal Fellow
Scientific Director: Wellcome Trust Centre for Neuroimaging
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.
https://www.fil.ion.ucl.ac.uk/~karl/
https://en.wikipedia.org/wiki/Karl_J._Friston
https://www.fil.ion.ucl.ac.uk/team/theoretical-neurobiology-team/
Lectures
how can we understand ourselves as sentient creatures? And what are the principles that underwrite sentient behaviour? This presentation uses the free energy principle to furnish an account in terms of active inference. First, we will try to understand sentience from the point of view of physics; in particular, the properties that self-organising systems—that distinguish themselves from their lived world—must possess. We then rehearse the same story from the point of view of a neurobiologist, trying to understand functional brain architectures. The narrative starts with a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if a system can be differentiated from its external milieu, then its internal and external states must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states. Crucially, this equips internal states with an information geometry, pertaining to probabilistic beliefs about something; namely external states. This free energy is the same quantity that is optimized in Bayesian inference and machine learning (where it is known as an evidence lower bound). In short, internal states will appear to infer—and act on—their world to preserve their integrity. This leads to a Bayesian mechanics, which can be neatly summarised as self-evidencing. In the second half of the talk, we will unpack these ideas using simulations of Bayesian belief updating in the brain and relate them to predictive processing and sentient behaviour.
Key words: active inference ∙ autopoiesis ∙ cognitive ∙ dynamics ∙ free energy ∙ epistemic value ∙ self-organization.
Topics
Theoretical NeuroscienceBiography
Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. He studied physics at the universities of Tubingen and Munich and received a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons, the dynamics of spiking neural networks and spike-timing dependent plasticity. More recently, he got interested in generalizations of Hebbian learning in the form of multi-factor learning rules and in the role of surprise for learning. He currently has a joint appointment at the School of Life Sciences and the School of Computer and Communications Sciences at the EPFL. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists. He is the recipient of the Valentino Braitenberg Award for Computational Neuroscience 2018 and a member of the Academy of Sciences and Literature Mainz (Germany).
Lectures
Abstract TBA
Topics
computational neuroscience, learning, memory, computational cognitive scienceBiography
http://cbl.eng.cam.ac.uk/pub/Public/Lengyel/Lengyel/bio.pdf
https://scholar.google.com/citations?user=WvgoL14AAAAJ&hl=en
Topics
Neuroscience; Neuroengineering; Artificial IntelligenceBiography
One of world’s leading computer science theorists, Christos Papadimitriou is best known for his work in computational complexity, helping to expand its methodology and reach. He has also explored other fields through what he calls the algorithmic lens, having contributed to biology and the theory of evolution, economics, and game theory (where he helped found the field of algorithmic game theory), artificial intelligence, robotics, networks and the Internet, and more recently the study of the brain.
He authored the widely used textbook Computational Complexity, as well as four others, and has written three novels, including the best-selling Logicomix and his latest,Independence. He considers himself fundamentally a teacher, having taught at UC Berkeley for the past 20 years, and before that at Harvard, MIT, the National Technical University of Athens, Stanford, and UC San Diego.
Papadimitriou has been awarded the Knuth Prize, IEEE’s John von Neumann Medal, the EATCS Award, the IEEE Computer Society Charles Babbage Award, and the Gödel Prize. He is a fellow of the Association for Computer Machinery and the National Academy of Engineering, and a member of the National Academy of Sciences.
Topics
Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data SciencesBiography
Panos Pardalos was born in Drosato (Mezilo) Argitheas in 1954 and graduated from Athens University (Department of Mathematics). He received his PhD (Computer and Information Sciences) from the University of Minnesota. He is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments.
Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.”
Panos Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher’s entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline.
Panos Pardalos is also a Member of several Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 600 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 71 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos
Panos Pardalos has lectured and given invited keynote addresses worldwide in countries including Austria, Australia, Azerbaijan, Belgium, Brazil, Canada, Chile, China, Czech Republic, Denmark, Egypt, England, France, Finland, Germany, Greece, Holland, Hong Kong, Hungary, Iceland, Ireland, Italy, Japan, Lithuania, Mexico, Mongolia, Montenegro, New Zealand, Norway, Peru, Portugal, Russia, South Korea, Singapore, Serbia, South Africa, Spain, Sweden, Switzerland, Taiwan, Turkey, Ukraine, United Arab Emirates, and the USA.
Topics
AI for physics and physics for AI.Biography
A native of Stockholm, Tegmark left Sweden in 1990 after receiving his B.Sc. in Physics from the Royal Institute of Technology (he’d earned a B.A. in Economics the previous year at the Stockholm School of Economics). His first academic venture beyond Scandinavia brought him to California, where he studied physics at the University of California, Berkeley, earning his M.A. in 1992, and Ph.D. in 1994.
After four years of west coast living, Tegmark returned to Europe and accepted an appointment as a research associate with the Max-Planck-Institut für Physik in Munich. In 1996 he headed back to the U.S. as a Hubble Fellow and member of the Institute for Advanced Study, Princeton. Tegmark remained in New Jersey for a few years until an opportunity arrived to experience the urban northeast with an Assistant Professorship at the University of Pennsylvania, where he received tenure in 2003.
He extended the east coast experiment and moved north of Philly to the shores of the Charles River (Cambridge-side), arriving at MIT in September 2004. He is married to Meia-Chita Tegmark and has two sons, Philip and Alexander.
Tegmark is an author on more than two hundred technical papers, and has featured in dozens of science documentaries. He has received numerous awards for his research, including a Packard Fellowship (2001-06), Cottrell Scholar Award (2002-07), and an NSF Career grant (2002-07), and is a Fellow of the American Physical Society. His work with the SDSS collaboration on galaxy clustering shared the first prize in Science magazine’s “Breakthrough of the Year: 2003.”
For more on his research, publications, and students, or his fun articles, goofs, and photo album, please visit Personal home page.
Topics
Computational Neuroscience, Nonlinear Dynamics, Statistical Physics, Cognitive ScienceBiography
Misha Tsodyks is a leading theoretical neuroscientist whose research has influenced important areas of neurobiology and the development of a quantitative understanding of brain functioning and human cognitive abilities. His work is focused on identifying neural algorithms that define functions of cortical systems and, in recent years, various aspects of cognitive behavior. From demonstrating the importance of sparsity in neural networks to providing deep insights into the mechanisms of short-term synaptic plasticity and working and associative memory, Tsodyks has devised conceptual models that make quantitative testable predictions for experiments.
https://www.ias.edu/scholars/tsodyks
https://www.weizmann.ac.il/brain-sciences/labs/tsodyks/
https://en.wikipedia.org/wiki/Misha_Tsodyks
Lectures
Tutorial Speakers
Topics
Neuroscience, Developmental Neurobiology, Human Brain DevelopmentLectures
Christina Kyrousi
1st Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Greece and University Mental Health, Neurosciences and Precision Medicine Research Institute “Costas Stefanis”
Neurodevelopmental disorders (NDDs) are a group of impairments that affect the development of the central nervous system leading to abnormal brain function during embryonic and early postnatal life. NDDs affect a great percentage of the population worldwide imposing a high societal and economical burden thus, interest in this field has grown in recent years as these disorders are of great medical importance. Nevertheless, the challenges of modeling them, due to the complexity of the development and the function of the human brain and the limitations in using human tissue in research are making their study difficult. Animal models play a central role in investigating the underlying molecular and cellular mechanisms of these disorders, however many of them display key differences regarding the human phenotype and in many cases, they partially or completely fail to recapitulate them. To overcome these limitations, in vitro two-dimensional (2D) human-specific models have been generated, however, they also present limitations, creating the need for a new approach to studying NDDs. The recent development of the three-dimensional (3D) brain organoids offers a promising approach as human-specific in vitro models for investigating these complex disorders. Advantages, limitations, and future applications of the in vivo and in vitro models that are used today to model NDDs are at the centre of the scientific interest.
Christina Kyrousi
1st Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Greece and University Mental Health, Neurosciences and Precision Medicine Research Institute “Costas Stefanis”
Cortical development depends on a coordinated sequence of events that includes neural progenitors’ proliferation and their subsequent differentiation into neurons and glial cells that will migrate from the neurogenic niche to colonize the developing cortical layers. These events can be regulated both from intrinsic cellular or genetic properties and from extrinsic stimuli that neural progenitors and neurons receive from their surroundings to regulate their fate and function. If any of the developmental processes is disrupted or altered, this leads to malformation of the cortical development (MCDs), such as heterotopias, alterations of the gyrification index of the cortex or total brain size changes. Interestingly, humans who are diagnosed with MCDs often develop other brain-related disorders with neurodevelopmental origin such as psychiatric disorders or brain ciliopathies. These observations suggest that convergent and divergent mechanisms may interplay in proper human neurogenesis or in the manifestation of brain-related disorders. It is, therefore, necessary to dissect their role at the cellular level. We hypothesize that seemingly completely different molecular, cellular, spatial, or temporal functions of genes will converge on higher-level organization units. These units could serve as organizing centres where different gene functions and mechanisms will be coordinated into a common response for modulating brain development. We aim to explore whether cilia, organelles like antennas that are known to intrinsically regulate brain development in mammals and serve as sensory organelles mediating environmental cues, could play an important role in brain disorders like MCDs and they would be master regulators of human brain development using animal models and human-specific brain organoids. This will allow us to unravel the etiology of MCDs, design therapeutic strategies and develop personalized medicine.