Seminars

Drawing on recent findings from evolutionary anthropology and neuroscience, professor Barton will lead us through the amazing story of the evolution of human cognition. Usingstatistical, phylogenetic analyses that tease apart the variation associated with different neural systems and due to different selection pressures, he will be addressing intriguing questions like ‘Why are there so many neurons in the cerebellum?’, ‘Is the neocortex the ‘intelligent’ bit of the brain?’, and ‘What explains that the recognition by humans of emotional expressions is disrupted by trancranial magnetic stimulation of the somatosensory cortex?’ Could, as professor Barton suggests, the cerebellum -modestly concealed beneath the volumetrically dominating neocortex and largely ignored- turn out to be the Cinderella of the study of brain evolution?

State-of-the-art machine vision models can predict human recognition memory for complex scenes with astonishing accuracy. In this talk I present work that investigated how memorable scenes are actually remembered and experienced by human observers. We found that memorable scenes were recognized largely based on recollection of specific episodic details but also based on familiarity for an entire scene. I thus highlight current limitations in machine vision models emulating human recognition memory, with promising opportunities for future research. Moreover, we were interested in what observers specifically remember about complex scenes. We thus considered the functional role of eye-movements as a window into the content of memories, particularly when observers recollected specific information about a scene. We found that when observers formed a memory representation that they later recollected (compared to scenes that only felt familiar), the overall extent of exploration was broader, with a specific subset of fixations clustered around later to-be-recollected scene content, irrespective of the memorability of a scene. I discuss the critical role that our viewing behavior plays in visual memory formation and retrieval and point to potential implications for machine vision models predicting the content of human memories.

This research panel explores how large and unconventional datasets can produce high-frequency inflation forecasts. Its starting point is renewed concern about inflation following substantial monetary and fiscal stimulus. Alberto Cavallo presents research on real-time inflation measurement. Yves Lemperiere, Adam Rej, José Scheinkman and Michael Woodford join the discussion of alternative data and its economic and financial applications. The panel connects timely measurement with the problem of assessing inflation while conventional economic statistics arrive with a delay.

Technological advances have increased the availability of recordings from large populations of neurons across multiple brain areas. Coupling these recordings with dimensionality reduction techniques, recent work has led to new proposals for how populations of neurons can send and receive signals selectively and flexibly. Advancement of these proposals depends, however, on untangling the bidirectional, parallel communication between neuronal populations. Because our current data analytic tools struggle to achieve this task, we have recently validated and presented a novel dimensionality reduction framework: DLAG, or Delayed Latents Across Groups. DLAG decomposes the time-varying activity in each area into within- and across-area latent variables. Across-area variables can be decomposed further into feedforward and feedback components using automatically estimated time delays. In this talk, I will review the DLAG framework. Then I will discuss new insights into the moment-by-moment nature of feedforward and feedback communication between visual cortical areas V1 and V2 of macaque monkeys. Overall, this work lays the foundation for dissecting the dynamic flow of signals across populations of neurons, and how it might change across brain areas and behavioral contexts.

Behavior arises from the coordinated activity of numerous distinct brain regions. Modern experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will then apply CURBD to understand the brain-wide flow of information leading to behavioral state transitions in larval zebrafish. These examples will establish CURBD as a flexible, scalable framework to infer brain-wide interactions that are inaccessible from experimental measurements alone.

Background: Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods: We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics measured at each of 180 cortical areas and seven subcortical structures. Linear mixed effect models were used to investigate associations between schizophrenia PRS and brain structure at global and regional scales, controlled for multiple comparisons. Results: Micro-structural phenotypes were more robustly associated with schizophrenia PRS than macro-structural phenotypes. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, and five subcortical structures. Other micro-structural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with schizophrenia PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate and prefrontal cortical areas, insula, and hippocampus. (Preprint: https://www.medrxiv.org/content/10.1101/2021.02.06.21251073v1)

Michael Chapman examines the gap between the refutation of Connes’ embedding conjecture by MIP*=RE and the construction of non-hyperlinear groups. The talk introduces non-local games, their values and Tsirelson’s problem, then associates a finitely presented *-algebra to a synchronous game. It explains how representations of this algebra relate to different game values, and why the relevant von Neumann closure cannot embed into an ultraproduct of the hyperfinite II₁ factor. A route to a non-hyperlinear group would require an appropriate counterexample algebra to be a complex group ring. Chapman introduces linear constraint system games, their associated game groups, and explains how the MIP*=RE construction differs from such a game.

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes the dynamic properties of the circuits and may promote brain oscillation. Notably, recent electrophysiological data observed in rodent V1 showed that the effect of visual experience on direction selectivity was similar to that observed in monkey data and provided strong validation of asymmetric changes of feedforward and recurrent synaptic strengths inferred from monkey data. This may suggest a general learning principle underlying the same computation, such as familiarity detection across different features represented in different brain regions.

Active particles which are self-propelled by converting energy into mechanical motion represent an expanding research realm in physics and chemistry. For micron-sized particles moving in a liquid (``microswimmers''), most of the basic features have been described by using the model of overdamped active Brownian motion [1]. However, for macroscopic particles or microparticles moving in a gas, inertial effects become relevant such that the dynamics is underdamped. Therefore, recently, active particles with inertia have been described by extending the active Brownian motion model to active Langevin dynamics which include inertia [2]. In this talk, recent developments of active particles with inertia (``microflyers'', ``hoppers'' or ``runners'') are summarized including: inertial delay effects between particle velocity and self-propulsion direction [3], tuning of the long-time self-diffusion by the moment of inertia [3], the influence of inertia on motility-induced phase separation and the cluster growth exponent [4], and the formation of active micelles (“rotelles”) by using inertial active surfactants. References [1] C. Bechinger, R. di Leonardo, H. Löwen, C. Reichhardt, G. Volpe, G. Volpe, Reviews of Modern Physics 88, 045006 (2016). [2] H. Löwen, Journal of Chemical Physics 152, 040901 (2020). [3] C. Scholz, S. Jahanshahi, A. Ldov, H. Löwen, Nature Communications 9, 5156 (2018). [4] S. Mandal, B. Liebchen, H. Löwen, Physical Review Letters 123, 228001 (2019). [5] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen, Surfactants and rotelles in active chiral fluids, will be published

Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic features of speech such as pitch, amplitude fluctuations and linguistic surprisal to neural measurements. We find neural correlates of speech processing both at the subcortical level, related to the pitch, as well as at the cortical level, related to amplitude fluctuations and linguistic structures. We also show that some of these measures allow to diagnose disorders of consciousness. Our findings may be applied in smart hearing aids that automatically adjust speech processing to assist a user, as well as in the diagnosis of brain disorders.

An emerging hypothesis in the field of Alzheimer’s disease (AD) is that neuronal hyperexcitability and other forms of aberrant network activity play an important role in shaping the clinical course of AD. In this talk, Dr. Lam will highlight the close and bi-directional relationships between epilepsy and AD, noting recent advances in our understanding of this topic spanning from animal models to humans. She will describe recent intracranial electrode recordings in humans that have revealed silent hippocampal epileptiform activity occurring in early stages of AD. Finally, she will discuss machine learning approaches that her laboratory has been developing to non-invasively diagnose and quantify silent hippocampal epileptiform activity from scalp EEG recordings.

Over 60 years of research has established that medial temporal lobe structures, including the hippocampus and entorhinal cortex, are necessary for the formation of episodic memories (i.e. memories of specific personal events that occur in spatial and temporal context). While prior work to establish the neural mechanisms underlying episodic memory has largely focused on questions related spatial context, recently we have begun to investigate how these brain structures could be involved in encoding aspects of temporal context. In particular, we have focused on how medial entorhinal cortex, a structure well known for its role in spatial memory, may also be involved in encoding interval time. To answer this question we have developed an instrumental paradigm for head-fixed mice that requires both immobile interval timing and locomotion-dependent navigation behavior. By combining this behavioral paradigm with large-scale cellular resolution functional imaging and optogenetic-mediated inactivation, our results suggest that MEC is required for learning of interval timing behavior and that interval timing could be mediated through regular, sequential neural activity of a distinct subpopulation of neurons in MEC that encode elapsed time during periods of immobility (Heys and Dombeck, 2018; Heys et al, 2020; Issa et al., 2020). In this talk, I will discuss these findings and discuss our on-going work to investigate the principles underlying the role of medial temporal lobe structures in timing behavior and episodic memory.

How flexible are association cortices? I will present a series of fMRI experiments addressing this question by investigating individuals born without hands, who use their feet as effectors to perform everyday actions. These results suggest that computations in association cortices are abstracted from visuomotor features and experience, similarly to the visual -independence of the association networks in people born blind, highlighting these regions’ ability to compensate for experience in any specific modality. These findings also open new avenues to utilize effector-independence in the action system for motor rehabilitation.

The microbiota-gut-brain axis is emerging as a research area of increasing interest for those investigating the biological and physiological basis of brain development and behaviour during early life, adolescence & ageing. The routes of communication between the gut and brain include the vagus nerve, the immune system, tryptophan metabolism, via the enteric nervous system or by way of microbial metabolites such as short chain fatty acids. Studies in animal models have shown that the development of an appropriate stress response is dependent on the microbiota. Developmentally, a variety of factors can impact the microbiota in early life including mode of birth delivery, antibiotic exposure, mode of nutritional provision, infection, stress as well as host genetics. Recently, the gut microbiota has been implicated in regulating the stress response, and social behaviour. Moreover, fundamental brain processes from adult hippocampal neurogenesis to myelination to microglia activation have been shown to be regulated by the microbiome. Further studies will focus on understanding the mechanisms underlying such brain effects and how they can be exploited by microbiota-targeted interventions including ‘psychobiotics’ and diet

Using Systems Neuroscience Approaches to Understand Motor Learning & Recovery Post-Stroke

Die Covid-19-Pandemie hat viele Menschen in der Schweiz unter grossen psychischen Druck gesetzt. Wie waren die unterschiedlichen Reaktionen auf diese Einschränkungen? Wie haben sich die Veränderungen im Alltag auf den Stresspegel ausgewirkt und die Psyche beeinflusst? Am Freitag, 19. März werden wir durch die Ergebnisse der Analyse aus der «Swiss Corona Stress Study» der Universität Basel mehr erfahren.