Each Lecturer will hold two/three lessons on a specific topic. The Lecturers below are confirmed.
TopicsAI, Reinforcement Learning, Neurotechnology, Computational Neuroscience, Behavior Analytics
Professor Aldo Faisal is the Professor of AI & Neuroscience at the Dept. of Computing and the Dept. of Bioengineering at Imperial College London. He was awarded a prestigious UKRI Turing AI Fellowship (£2 Mio including industry partners). Aldo is the Founding Director of the £20Mio. UKRI Centre for Doctoral Training in AI for Healthcare that aims to transform AI for Healthcare research and pioneer training 100 PhD and Clinical PhD Fellows. He also holds a Chair in Digital Healthcare at the University of Bayreuth (Germany).
At his two departments, Aldo leads the Brain & Behaviour Lab focussing on AI & Neuroscience and the Behaviour Analytics Lab at the Data Science Institute. He is Associate Investigator at the MRC London Institute of Medical Sciencesand is affiliated faculty at the Gatsby Computational Neuroscience Unit (University College London). He was the first elected Speaker of the Cross-Faculty Network in Artificial Intelligence representing AI in College on behalf of over 200 academic members.
Aldo serves as an Associate Editor for Nature Scientific Data and PLOS Computational Biology and has acted as conference chair, program/area chair, chair in key conferences in the field (e.g. Neurotechnix, KDD, NIPS, IEEE BSN). In 2016 he was elected into the Global Futures Council of the World Economic Forum.
Aldo received a number of awards and distinctions, including Scholar of the German National Merit Foundation (Studienstiftung des Deutsche Volkes; Undergraduate & PhD), a PhD Fellow of the Böhringer-Ingelheim Foundation for Basic Biomedical Research, elections as a Junior Research Fellow at the University of Cambridge (Wolfson College), and a number of research prizes and award such as the Toyota Mobility Foundation $50,000 Research Discovery Prize in 2018, and together with the AI Clinician team the Rosetree Interdisciplinary Award (£300,000) in 2022.
Aldo’s lab featured regularly across global media (such as BBC, CNN, TED, TEDx, Wall Street Journal, Guardian, Financial Times , WIRED, Scientific American, New Scientist, etc.), e.g. in 2016 Scientific American voted his research on gaze-based control as 1st of 10 most transformative ideas of year.
Dr Faisal’s labs is operated as a borderless lab across UK and Germany. The UK lab at Imperial is located in the Royal School of Mines building and combine cross-disciplinary computational and experimental approaches to investigate how the brain and behaviour evolved to learn and control goal-directed behaviour. The neuroscientific findings enable the targeted development of novel technology for clinical and research applications (Neurotechnology) for a variety of neurological/motor disorders and amputees. Key techniques include on the computational side are data-driven methods from machine learning & stochastic modelling techniques and experimentally we use sensorimotor experiments, eye-tracking & kinematics (full-body, hands), non-invasive brain imaging (EEG, fNIRS), robotics (hand & arm robots).
Professor Karl J. Friston MB, BS, MA, MAE, MRCPsych, FMedSci, FRSB, FRS
Scientific Director: Wellcome Centre for Human Neuroimaging
Institute of Neurology, UCL
12 Queen Square
London. WC1N 3AR UK
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.
Prof. Friston’s Google Scholar
Abstract: This presentation considers deep temporal models in the brain. It builds on previous formulations of active inference to simulate behavior and electrophysiological responses under deep (hierarchical) generative models of discrete state transitions. The deeply structured temporal aspect of these models means that evidence is accumulated over distinct temporal scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behavior in terms of Bayesian belief updating – and associated neuronal processes – to reproduce the epistemic foraging seen in reading. These simulations reproduce these sort of perisaccadic delay period activity and local field potentials seen empirically; including evidence accumulation and place cell activity. These simulations are presented as an example of how to use basic principles to constrain our understanding of system architectures in the brain – and the functional imperatives that may apply to neuronal networks.
Key words: active inference ∙ insight ∙ novelty ∙ curiosity ∙ model reduction ∙ free energy ∙ epistemic value ∙ structure learning
Abstract: This talk offers a formal account of insight and learning in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how agents learn from a small number of ambiguous outcomes to form insight. I will use simulations of abstract rule-learning and approximate Bayesian inference to show that minimising (expected) free energy leads to active sampling of novel contingencies. This epistemic, curiosity-directed behaviour closes `explanatory gaps’ in knowledge about the causal structure of the world; thereby reducing ignorance, in addition to resolving uncertainty about states of the known world. We then move from inference to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries in their generative models of the world. The ensuing Bayesian model reduction evokes mechanisms associated with sleep and has all the hallmarks of aha moments.
Key words: active inference ∙ insight ∙ novelty ∙ curiosity ∙ model reduction ∙ free energy ∙ epistemic value
TopicsQuantitative Neuroscience, Mathematical Neuroscience, Computational Neuroscience, Neural Coding, Systems Neuroscience
I studied mathematics at Cambridge University, did a PhD in robotics at UCL, then moved to Rutgers University in the United States for postdoctoral work in neuroscience. Before returning to UCL in 2012, I was Associate Professor of Neuroscience at Rutgers, and Professor of Neurotechnology at Imperial College London. I am currently Professor of Quantitative Neuroscience in the UCL Institute of Neurology, and together with Matteo Carandini direct the Cortexlab.
The research in the Moran Lab focuses on computational neuroscience, computational psychiatry and computational neurology. In particular, the Moran Lab aims to join together brain connectivity analysis with their algorithmic role; i.e. what information brain connections relay. This work lies at the intersection of artificial intelligence (deep networks), Bayesian inference (variational principles) and experimental neurobiology (cognitive tasks in the scanner). Of particular interest are the role of families of neurotransmitters, such as noradrenaline, dopamine and serotonin, in prediction errors and model-based decision making. The Moran Lab uses the free energy principle as a principle to develop new methods in artificial intelligence and in disease modeling, focusing on age-related neurodegenerative disease and schizophrenia. Dr. Moran also serves as an editor for the Neuroimage and Neuroimage Clinical journals.
TopicsNeuroscience, Computational Neuroscience, Emotion, Memory, Vision
Edmund T. Rolls is at the Oxford Centre for Computational Neuroscience, Oxford, and at the Department of Computer Science, University of Warwick, UK, where he is a Professor in Computational Neuroscience, and is focussing on full-time research. Edmund Rolls is also a specially-appointed Professor at the Institute of Science and Technology for Brain-Inspired Intelligence at Fudan University, Shanghai.
Before this, Edmund Rolls was Professor of Experimental Psychology at The University of Oxford, and Fellow and Tutor in Psychology at Corpus Christi College, Oxford (1973-2008; Vice President of Corpus Christi College 2003-2006).
Edmund Rolls is the 18th most cited scientist in the UK, and the 150th most cited scientist in the world out of 6,880,389 in any field of science who have published more than 5 papers across every scientific field (i.e. in the top 0.002%) (composite indicator c, Ioannidis et al 2019 A standardized citation metrics author database annotated for scientific field. PLoS Biol 17(8): e3000384). Edmund Rolls is also the 20th most cited neuroscientist in the world, and 3rd in the UK (composite indicator c for Neurology and Neurosurgery, Ioannidis et al 2019).
TopicsArtificial Intelligence, Multi-Agent Systems, Nowledge Representation
I am a Professor of Computer Science in the Department of Computer Science at the University of Oxford, and a Senior Research Fellow at Hertford College. From 2014-21 I was Head of Department of Computer Science. I joined Oxford on 1 June 2012; before this I was for twelve years a Professor of Computer Science at the University of Liverpool.
In 2020, I was awarded the Lovelace Medal from the British Computer Society; in 2006, I was the recipient of the ACM Autonomous Agents Research Award; in 2021 I received the AAAI/EAAI Outstanding Educator Award; and in 2021 I received a Turing AI World Leading Researcher Fellowship from UKRI.
I am an ACM Fellow, a AAAI Fellow, a EURAI Fellow, an AISB Fellow, a BCS Fellow, and a member of Academia Europaea.
I served as President of the International Joint Conference on Artificial Intelligence (IJCAI) from 2015-17, President of the European Association for AI (EurAI) from 2014-16, and President of the International Association for Autonomous Agents and Multiagent Systems (IFAAMAS) from 2007-09.
I was program chair for the 19th European Conference on Artificial Intelligence (ECAI-2010), held in Lisbon, Portugal, in August 2010. I was Conference Chair for the 24th International Joint Conference on Artificial Intelligence (IJCAI-2015), held in Buenos Aires, Argentina, in July 2015.
In 1997, I founded AgentLink, the EC-funded European Network of Excellence in the area of agent-based computing. In October 2011, I was awarded a 5-year ERC Advanced Grant, entitled “Reasoning About Computational Economies” (RACE).
Between 2003 and 2009 I was co-editor-in-chief of the Journal Autonomous Agents and Multi-Agent Systems. I have served as an associate editor of the Journal of Artificial Intelligence Research (JAIR) (2006-2009, 2009-2012), an associate editor of Artificial Intelligence journal (2009-2012) and I currently serve on the editorial boards of the Journal of Applied Logic, Journal of Logic and Computation, Journal of Applied Artificial Intelligence, Games, and Computational Intelligence.
I have written two popular science introductions to AI: the Ladybird Expert Guide to Artificial Intelligence, a short overview in the iconic British book series, and The Road to Conscious Machines, a longer introduction to AI in Penguin’s classic Pelican series; an edited and revised version of The Road to Conscious Machines was published in the USA by Flatiron Press under the title A Brief History of AI. Both books are aimed a general audience.