6, 9 and 21/10/20 @ 11:30am
06/10/2020: The Vigilant Sleeper: Sensory Processing and Learning during Sleep
Sleep suppresses the ability to react to environmental demands. It has been proposed that a phenomenon of sensory isolation, whereby sensory inputs fail to reach cortical brain regions during sleep, would be responsible for this absence of responses. How and why this decoupling is implemented has been intensively investigated. However, sleepers might not be fully disconnected from their environment. Empirical evidence shows that sleepers can perform a surprisingly large range of cognitive processes, including the extraction of meaning, the preparation of motor responses and the learning of new information. I will describe potential mechanisms explaining sleepers’ ability to maintain covert cognitive processes as well as their suppression. Rather than being isolated from the environment, sleepers seem to enter a standby mode, allowing them to balance the monitoring of their surroundings with sensory isolation. This balance could allow sleepers to determine when to stay asleep or when to wake up, and might be essential for the fulfilment of sleep functions, notably memory consolidation.
Sleep has been classically described as an all-or-nothing global phenomenon. However, recent research strongly suggests that this view requires tempering.I will present here evidence, from invasive and non-invasive recordings in animals and humans, show that neural activity typically associated with sleep can locally occur during wakefulness. I will also show how these sleep intrusions, occurring spontaneously and modulated by pharmacological means, are associated with impaired performance during cognitive tasks. I will also discuss the phenomenology of local sleep (i.e., what it feels like when your brain is partially asleep). Based on these findings, I will propose that occurrences of local sleep could represent the neural mechanism underlying many attentional lapses. In particular, I will argue that a unique physiological event such as local sleep could account for a diversity of behavioral and phenomenological outcomes from sluggish to impulsive responses, mind wandering and mind blanking. I will finally show how the local sleep framework can be extended to examine inter-individual differences by focusing on a clinical population with known sleep and attentional disorders: ADHD.
Polysomnographic (PSG) recordings are the gold-standard of sleep research in humans. Yet, PSG recordings are not always analysed to their full potential. This is particularly striking in the case of insomnia, a disorder for which PSG recordings are not necessary to establish a diagnosis. I will show here how PSG recordings carry very rich and reliable information about one's sleep, in particular in the case of insomnia. In fact, the paradox of SSM (presence of subjective symptoms of insomnia without objective impairments of sleep) is apparent only when focusing on superficial, large-scale metrics of sleep but dissolves when examining the finer dynamics of brain activity. The limitations outlined in the past and leading to the exclusion of PSG recordings from the diagnosis of insomnia are no longer warranted and clinicians should revisit the benefits of PSG recordings. This reassessment is timely in the light of two ongoing revolutions in the field of Sleep Medicine: (1) the emergence of consumer-based PSG devices will bring PSG recordings to the masses, (2) the translation of computational tools from the field of Artificial Intelligence to Sleep Medicine allows the rapid, automated and massive analysis of large datasets. I will illustrate the advantages of these new methods in the field of Sleep Research by showing how we can even move beyond the current classification of sleep stages using unsupervised clustering.
28/01/20 @ 9am
The neural representation of mental states: Organization for prediction
To navigate the social world, people must understand and anticipate each other’s thoughts and feelings. How does the brain organize its representations of these hidden mental states? In the first part of my talk, I will describe the 3d Mind Model, which posits that three psychological dimensions describe the way the brain represents mental states: rationality (vs. emotionality), social impact (the extent to which states affect others), and valence (positive vs. negative). FMRI, computational text analysis, and behavior all indicate that the 3d Mind Model is a robust, comprehensive, and generalizable account of mental state representation. In the second part of my talk, I will discuss a key function of this map of the mental world: facilitating the prediction of mental state dynamics. I will present evidence from experience sampling and fMRI studies which indicates that representing mental states along the dimensions of the 3d Mind Model facilitates accurate and efficient social prediction. I will conclude by discussing new data from statistical learning and artificial neural network experiments, which suggest that the goal of prediction shapes how the brain generates mental state concepts in the first place.
Professor of Cognitive Neuroscience at UCLA
Clinical neuropsychologist with a broad interest in the study of human cognition in relation to brain structure, function, and pathology. Her experimental expertise includes structural and functional MRI and intraoperative electrocortical stimulation mapping, as well as classical neuropsychological approaches.
Jack Van Horn
Professor of Psychology and Data Science at University of Virginia
Author, researcher, lecturer on the human brain, its structure and function, and the role of information technology in sharing data for use in understanding fundamental neurological processes in health and disease.
King’s College London
After completing his studies in psychology at the University of Padua (1999), he carried out a PhD in neuroscience at University College London (2002). In 2004, he joined the Institute of Psychiatry, Psychology & Neuroscience at King's College London, where is Professor of Early Intervention in Mental Health. His research Interests: Integration of machine learning and neuroimaging to develop diagnostic and prognostic models of psychosis; Development and validation of novel clinical tools for improving detection and treatment of psychosis; Use of smartphone technologies to investigate the impact of the built and social environment on mental health in real time (see urbanmind.info).
Università degli Studi di Milano
Professor of Philosophy of Science at University of Milan since 2001. Before that he studied at the Husserl-Archives of Leuven (1992-1993), at the Ecole Normale Superiéure of Paris (1994), and at the University of Genova (1995-1999), where he obtained my PhD in Philosophy of Science. Fields of research: Cognitive neuroscience and philosophy of mind. He is currently working on the role of motor processes and representations in joint action.
Past Visiting Professors
Professor of Psychology and director of the Stanford Center for Reproducible Neuroscience. His research uses brain imaging to understand how we learn and make decisions and how we exert self-control. Some projects he developed include the Cognitive Atlas (htttp://www.cognitiveatlas.org) and OpenfMRI (http://www.openfmri.org).
University of California, Davis
American neuroscientist, Professor of Psychology and head of the Laboratory of Evolutionary Neurobiology. Her research interests center on how complex brains in mammals (e.g., humans) evolve from simpler forms.
Netherlands Institute for Neuroscience
Hans Op de Beeck
Maria Ida Gobbini
Boston Children's Hospital