This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

One of the most common medical conditions you’ve (maybe) never heard of – functional neurological disorder – lays at the interface of neurology and psychiatry and offers a window into fundamental brain-mind-body processes. Across ancient and modern times, functional neurological disorder has had a long and tumultuous history, with an evolving debate and understanding of how biopsychosocial factors contribute to the manifestation of the disorder. A central issue in contemporary discussions has revolved around questioning the extent to which emotions play a mechanistic and aetiological role in functional neurological disorder. Critical in this context, however, is that this ongoing debate has largely omitted the question of what emotions are in the first place. This talk first brings together advances in the understanding of working principles of the brain fundamental to introducing a new understanding of what emotions are. Building on recent theoretical frameworks from affective neuroscience, the idea of how the predictive process of emotion construction can be an integral component of the pathophysiology of functional neurological disorder is discussed.

Living Systems often seem to follow, in addition to external constraints and interactions, an intrinsic predictive model of the world — a defining trait of Anticipatory Systems. Here we study rhythmic behaviour in Caulerpa, a marine green alga, which appears to predict the day/night light cycle. Caulerpa consists of differentiated organs resembling leaves, stems and roots. While an individual can exceed a meter in size, it is a single multinucleated giant cell. Active transport has been hypothesized to play a key role in organismal development. It has been an open question in the literature whether rhythmic transport phenomena in this organism are of autonomous circadian nature. Using Raspberry-Pi cameras, we track over weeks the morphogenesis of tens of samples concurrently, while tracing at resolution of tens of seconds the variation of the green coverage. The latter reveals waves propagating over centimeters within few hours, and is attributed to chloroplast redistribution at whole-organism scale. Our observations of algal segments regenerating under 12-hour light/dark cycles indicate that the initiation of the waves precedes the external light change. Using time-frequency analysis, we find that the temporal spectrum of these green pulses contains a circadian period. The latter persists over days even under constant illumination, indicative of its autonomous nature. We further explore the system under non-circadian periods, to reveal how the spectral content changes in response. Time-keeping and synchronization are recurring themes in biological research at various levels of description — from subcellular components to ecological systems. We present a seemingly primitive living system that exhibits apparent anticipatory behaviour. This research offers quantitative constraints for theoretical frameworks of such systems.

Many of us came to biology from physics. There we have been trained on such classic examples as muon g-2, where experimental data and theoretical predictions agree to many significant digits. Now, working in biology, we routinely hear that it is messy, most details matter, and that the best hope for theory in biology is to be semi-qualitative, predict general trends, and to forgo the hope of ever making quantitative predictions with the precision that we are used to in physics. Colloquially, we should be satisfied even if data and models differ so much that plotting them on the same plot makes little sense. However, some of us won’t be satisfied by this. So can we take theory in biology seriously and predict experimental outcomes within (small) error bars? Certainly, we won’t be able to predict everything, but this is never required, even in traditional physics. But we should be able to choose some features of data that are nontrivial and interesting, and focus on them. We also should be able to find different classes of models --- maybe even null models --- that match biology better, and thus allow for a better agreement. It is even possible that large-dimensional datasets of modern high-throughput experiments, and the ensuing “more is different” statistical physics style models will make quantitative, precise theory easier. To explore the role of quantitative theory in biology, in this workshop, eight speakers will address some of the following general questions based on their specific work in different corners of biology: Which features of biological data are predictable? Which types of models are best suited to making quantitative predictions in different fields? Should theorists interested in quantitative predictions focus on different questions, not typically asked by biologists? Do large, multidimensional datasets make theories (and which theories?) more or less likely to succeed? This will be an unapologetically theoretical physics workshop — we won’t focus on a specific subfield of biology, but will explore these questions across the fields, hoping that the underlying theoretical frameworks will help us find the missing connections.

We will organize the workshop around one question: “Now that we can track (most) everything, what can we do with the data?” Given the recent dramatic advances in technology, we now have behavioral data sets with orders of magnitude more accuracy, dimensionality, diversity, and size than we had even a few years ago. That being said, there is still little agreement as to what theoretical frameworks can inform our understanding of these data sets and suggest new experiments we can perform. We hope that after this workshop we’ll see a variety of new ideas and perhaps gain some inspiration. We have invited eight speakers, each studying different systems, scales, and topics, to provide 10 minute presentations focused on the above question, with another 10 minutes set aside for questions/discussions (moderated by the two of us). Although we naturally expect speakers to include aspects of their own work, we have encouraged all of them to think broadly and provocatively. We are also hoping to organize some breakout sessions after the talks so that we can have some more expanded discussions about topics arising during the meeting.