Uploaded December 2023 | Updated September 2026, 1 week ago
Daniel Wolpert, Columbia University
Abstract: Humans spend a lifetime learning, storing and refining a repertoire of motor memories appropriate for the multitude of tasks we perform. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. I will review our recent work on how humans learn to make skilled movements focusing on how statistical learning can lead to multi-modal object representations, how we represent the dynamics of objects, the role of context in the expression, updating and creation of motor memories and how families of objects are learned.
Bio: Daniel Wolpert FMedSci FRS. Daniel qualified as a medical doctor in 1989. He worked with John Stein and Chris Miall in the Physiology Department of Oxford University where he received his D.Phil. in 1992. He worked as a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT in Mike Jordan's group and in 1995 joined the Sobell Department of Motor Neuroscience, Institute of Neurology as a Lecturer. In 2005 moved to the University of Cambridge where he was Professor of Engineering (1875) and a fellow of Trinity College and from 2013 the Royal Society Noreen Murray Research Professorship in Neurobiology. In 2018 Daniel joined the Zuckerman Mind Brain and Behavior Institute at Columbia University as Professor of Neuroscience and is vice-chair of the Department of Neuroscience. Daniel retains a part-time position as Director of Research at the Department of Engineering, University of Cambridge.
He was elected a Fellow of the Academy of Medical Sciences in 2004 and a Fellow of the Royal Society in 2012.
He was awarded the Royal Society Francis Crick Prize Lecture (2005), the Minerva Foundation Golden Brain Award (2010), the Royal Society Ferrier Medal (2020) and gave the Fred Kavli Distinguished International Scientist Lecture at the Society for Neuroscience (2009).
https://cbmm.mit.edu/news-events/events/quest-cbmm-seminar-series-statistical-learning-human-sensorimotor-control
Daniel Wolpert, Columbia University
Abstract: Humans spend a lifetime learning, storing and refining a repertoire of motor memories appropriate for the multitude of tasks we perform. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. I will review our recent work on how humans learn to make skilled movements focusing on how statistical learning can lead to multi-modal object representations, how we represent the dynamics of objects, the role of context in the expression, updating and creation of motor memories and how families of objects are learned.
Bio: Daniel Wolpert FMedSci FRS. Daniel qualified as a medical doctor in 1989. He worked with John Stein and Chris Miall in the Physiology Department of Oxford University where he received his D.Phil. in 1992. He worked as a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT in Mike Jordan's group and in 1995 joined the Sobell Department of Motor Neuroscience, Institute of Neurology as a Lecturer. In 2005 moved to the University of Cambridge where he was Professor of Engineering (1875) and a fellow of Trinity College and from 2013 the Royal Society Noreen Murray Research Professorship in Neurobiology. In 2018 Daniel joined the Zuckerman Mind Brain and Behavior Institute at Columbia University as Professor of Neuroscience and is vice-chair of the Department of Neuroscience. Daniel retains a part-time position as Director of Research at the Department of Engineering, University of Cambridge.
He was elected a Fellow of the Academy of Medical Sciences in 2004 and a Fellow of the Royal Society in 2012.
He was awarded the Royal Society Francis Crick Prize Lecture (2005), the Minerva Foundation Golden Brain Award (2010), the Royal Society Ferrier Medal (2020) and gave the Fred Kavli Distinguished International Scientist Lecture at the Society for Neuroscience (2009).
https://cbmm.mit.edu/news-events/events/quest-cbmm-seminar-series-statistical-learning-human-sensorimotor-control


![Efficient representation, learning, and planning through abstraction: clustering cognitive spaces...
[full title] Efficient representation, learning, and planning through abstraction: clustering cognitive spaces into submaps
Ila Fiete, MIT
Abstract: Episodic memory involves fragmenting the continuous stream of experience into discrete episodes. Not coincidentally, the hippocampus, which plays a central role in both episodic memory and spatial navigation, represents large spatial environments in a fragmented way even when explored in a continuous trajectory. In non-spatial and non-memory contexts too, humans report sudden contextual re-anchoring or re-orientation when reading garden path sentences (“Time flies like an arrow, fruit flies like a banana.) or watching a movie with viewpoint changes. In this talk, I will describe a theory for the online and real-time generation of fragmented representations and contextual re-anchoring from continuous experience that resemble those obtained by principled but offline and computationally complex information-based algorithms. The resulting fragmentations closely match those observed from neural recordings in animals navigating through complex environments. I will discuss the utility of map fragmentation, as a form of state abstraction that enables representation fidelity, flexible and rapid learning through reuse of existing fragments, and many-fold improvements in the ability to plan and navigate through complex environments relative to more global representations. Efficient representation, learning, and planning through abstraction: clustering cognitive spaces...](https://i.ytimg.com/vi/gfgoLjhrh7k/mqdefault.jpg)







