Seminars

After more than 4 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stages -- if vaccination progresses too slowly -- mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. Our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when tonic (baseline) LC activity increases but are enhanced when external events drive phasic LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

The role of attention in working memory remains controversial, but there is some agreement on the notion that the focus of attention holds mnemonic representations in a privileged state of heightened accessibility in working memory, resulting in better memory performance for items that receive focused attention during retention. Closely related, representations held in the focus of attention are often observed to be robust and protected from degradation caused by either perceptual interference (e.g., Makovski & Jiang, 2007; van Moorselaar et al., 2015) or decay (e.g., Barrouillet et al., 2007). Recent findings indicate, however, that representations held in the focus of attention are particularly vulnerable to degradation, and thus, appear to be particularly fragile rather than robust (e.g., Hitch et al., 2018; Hu et al., 2014). The present set of experiments aims at understanding the apparent paradox of information in the focus of attention having a protected vs. vulnerable status in working memory. To that end, we examined the effect of perceptual interference on memory performance for information that was held within vs. outside the focus of attention, across different ways of bringing items in the focus of attention and across different time scales.

Hydra is an extraordinary creature. Continuously replacing itself, it can live indefinitely, performing a stable repertoire of reasonably sophisticated behaviors. This remarkable stability under plasticity may be due to the uniform nature of its nervous system, which consists of two apparently noncommunicating nerve net layers. We use modeling to understand the role of active muscles and biomechanics interact with neural activity to shape Hydra behaviour. We will discuss our findings and thoughts on how this simple nervous system may self-organize to produce purposeful behavior.

Our world is full of living creatures that must share information to survive and reproduce. As humans, we easily forget how hard it is to communicate within natural environments. So how do organisms solve this challenge, using only natural resources? Ideas from computer science, physics and mathematics, such as energetic cost, compression, and detectability, define universal criteria that almost all communication systems must meet. We use insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen’s location.

Tutorial on Notebook workflows for reproducible data science.

Diverse physical processes played important roles in the origin of life. I will review the origin of molecular homochirality, the growth of protocell membranes, and potential roles for liquid-liquid phase separation. I will then discuss the concept of functional information and its quantitative relationship with molecular function.

Natural systems provide both puzzles to unravel and demonstrations of what's possible. The natural world is full of complex systems of dynamically interchangeable, individually unreliable components that produce effective and reliable outcomes at the group level. A complementary goal to understanding the operation of such systems is that of being able to engineer artifacts that work in a similar way. One notable type of collective behavior is collective construction, epitomized by mound-building termites, which build towering, intricate mounds through the joint activity of millions of independent and limited insects. The artificial counterpart would be swarms of robots designed to build human-relevant structures. I will discuss work on both aspects of the problem, including studies of cues that individual termite workers use to help direct their actions and coordinate colony activity, and development of robot systems that build user-specified structures despite limited information and unpredictable variability in the process. These examples illustrate principles used by the insects and show how they can be applied in systems we create.

Homeostatic plasticity plays a key role in stabilizing neural network activity. But what is its role in neural information processing? We showed analytically how homeostasis changes collective dynamics and consequently information flow - depending on the input to the network. We then studied how input and homeostasis on a recurrent network of LIF neurons impacts information flow and task performance. We showed how we can tune the working point of the network, and found that, contrary to previous assumptions, there is not one optimal working point for a family of tasks, but each task may require its own working point.

As tiny robots become individually more sophisticated, and larger robots easier to mass produce, a breakdown of conventional disciplinary silos is enabling swarm engineering to be adopted across scales and applications, from nanomedicine to treat cancer, to cm-sized robots for large-scale environmental monitoring or intralogistics. This convergence of capabilities is facilitating the transfer of lessons learned from one scale to the other. Cm-sized robots that work in the 1000s may operate in a way similar to reaction-diffusion systems at the nanoscale, while sophisticated microrobots may have individual capabilities that allow them to achieve swarm behaviour reminiscent of larger robots with memory, computation, and communication. Although the physics of these systems are fundamentally different, much of their emergent swarm behaviours can be abstracted to their ability to move and react to their local environment. This presents an opportunity to build a unified framework for the engineering of swarms across scales that makes use of machine learning to automatically discover suitable agent designs and behaviours, digital twins to seamlessly move between the digital and physical world, and user studies to explore how to make swarms safe and trustworthy. Such a framework would push the envelope of swarm capabilities, towards making swarms for people.

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges, to choosing with whom to associate. Here, using an integrated theoretical and experimental approach (employing immersive Virtual Reality), with both invertebrate and vertebrate models—the fruit fly, desert locust and zebrafish—we consider the recursive interplay between movement and collective vectorial integration in the brain during decision-making regarding options (potential ‘targets’) in space. We reveal that the brain repeatedly breaks multi-choice decisions into a series of abrupt (critical) binary decisions in space-time where organisms switch, spontaneously, from averaging vectorial information among, to suddenly excluding one of, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Close to each bifurcation the ‘susceptibility’ of the system exhibits a sharp increase, inevitably causing small differences among the remaining options to become amplified; a property that both comes ‘for free’ and is highly desirable for decision-making. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Episodic memory performance decreases with advancing age. According to theoretical models, such memory decline might be a consequence of age-related reductions in the ability to form distinct neural representations of our past. In this talk, I want to present our new age-comparative fMRI study investigating age-related neural dedifferentiation across different representational levels. By combining univariate analyses and searchlight pattern similarity analyses, we found that older adults show reduced category selective processing in higher visual areas, less specific item representations in occipital regions and less stable item representations. Dedifferentiation on all these representational levels was related to memory performance, with item specificity being the strongest contributor. Overall, our results emphasize that age-related dedifferentiation can be observed across the entire cortical hierarchy which may selectively impair memory performance depending on the memory task.

Analogy offers a new interpretation of a common concern in ethics: whether decision making includes or excludes a consideration of moral issues. This is often discussed as the moral awareness of decision makers and considered a motivational concern. The possible new interpretation is that moral awareness is in part a matter of expertise. Some failures of moral awareness can then be understood as stemming from novicehood. Studies of analogical transfer are consistent with the possibility that moral awareness is in part a matter of expertise, that as a result motivation is less helpful than some prior theorizing would predict, and that many adults are not as expert in the domain of ethics as one might hope. The possibility of expert knowledge of ethical principles leads to new questions and opportunities.

Perception of object motion is fundamentally multisensory, yet little is known about similarities and differences in the computations that give rise to our experience across senses. Insight can be provided by examining auditory motion processing in early blind individuals. In those who become blind early in life, the ‘visual’ motion area hMT+ responds to auditory motion. Meanwhile, the planum temporale, associated with auditory motion in sighted individuals, shows reduced selectivity for auditory motion, suggesting competition between cortical areas for functional role. According to the metamodal hypothesis of cross-modal plasticity developed by Pascual-Leone, the recruitment of hMT+ is driven by it being a metamodal structure containing “operators that execute a given function or computation regardless of sensory input modality”. Thus, the metamodal hypothesis predicts that the computations underlying auditory motion processing in early blind individuals should be analogous to visual motion processing in sighted individuals - relying on non-separable spatiotemporal filters. Inconsistent with the metamodal hypothesis, evidence suggests that the computational algorithms underlying auditory motion processing in early blind individuals fail to undergo a qualitative shift as a result of cross-modal plasticity. Auditory motion filters, in both blind and sighted subjects, are separable in space and time, suggesting that the recruitment of hMT+ to extract motion information from auditory input includes a significant modification of its normal computational operations.

During our everyday lives, much of what we experience is familiar and predictable. We typically follow the same morning routine, take the same route to work, and encounter the same colleagues. However, every once in a while, we encounter a surprising event that violates our expectations. When we encounter such violations of our expectations, it is adaptive to update our internal model of the world in order to make better predictions in the future. The hippocampus is thought to support both the learning of the predictable structure of our environment, as well as the detection and encoding of violations. However, the hippocampus is a complex and heterogeneous structure, composed of different subfields that are thought to subserve different functions. As such, it is not yet known how the hippocampus accomplishes the learning and updating of structured knowledge. Using behavioral methods and high-resolution fMRI, I'll show that during learning of repeated and predicted events, hippocampal subfields differentially integrate and separate event representations, thus learning the structure of ongoing experience. I then move on to discuss how when events violate our predictions, there is a shift in communication between hippocampal subfields, potentially allowing for efficient encoding of the novel and surprising information. If time permits, I'll present an additional behavioral study showing that violations of predictions promote detailed memories. Together, these studies advance our understanding of how we adaptively learn and update our knowledge.