Complexity Thoughts: Issue #16
Unraveling complexity: building knowledge, one paper at a time
If you find Complexity Thoughts, click on the Like button, leave a comment, repost on Substack or share this post. It is the only feedback I can have for this free service.
The frequency and quality of this newsletter relies on social interactions. Thank you!
Dall-e 3 representation of this issue’s content
In a nutshell
Lot of food for thoughts in this issue. Manipulating magnetic microdisks reveals the intriguing possibilities of nonreciprocal interactions for microrobotics, with unexpected behaviors. In the ecological realm, empirical studies on mutualistic networks unveil how indirect effects are the puppet masters behind species fitness. Coral reef fishes offer a lesson in robust social information networks, while advanced encoding methods like CEBRA bridge behavioral actions to neural dynamics. Cellular Automata are (unexpected) heroes in reservoir computing. But amid these advances, a climate alarm rings: the AMOC's potential mid-century collapse warns us of imminent tipping points. These studies collectively underscore the intricate choreography of elements—from neurons to climates—in shaping complex adaptive systems.
Network & Complex Systems foundations
On-Demand Breaking of Action-Reaction Reciprocity between Magnetic Microdisks Using Global Stimuli
Physics meets directed networks at a fundamental level:
Coupled physical interactions induce emergent collective behaviors of many interacting objects. Nonreciprocity in the interactions generates unexpected behaviors. There is a lack of experimental model system that switches between the reciprocal and nonreciprocal regime on demand. Here, we study a system of magnetic microdisks that breaks action-reaction reciprocity via fluid-mediated hydrodynamic interactions, on demand. Via experiments and simulations, we demonstrate that nonreciprocal interactions generate self-propulsion-like behaviors of a pair of disks; group separation in collective of magnetically nonidentical disks; and decouples a part of the group from the rest. Our results could help in developing controllable microrobot collectives. Our approach highlights the effect of global stimuli in generating nonreciprocal interactions.
Ecological Systems
Indirect effects shape species fitness in coevolved mutualistic networks
Ecological interactions are one of the main forces that sustain Earth’s biodiversity. A major challenge for studies of ecology and evolution is to determine how these interactions affect the fitness of species when we expand from studying isolated, pairwise interactions to include networks of interacting species1,2,3,4. In networks, chains of effects caused by a range of species have an indirect effect on other species they do not interact with directly, potentially affecting the fitness outcomes of a variety of ecological interactions (such as mutualism)5,6,7. Here we apply analytical techniques and numerical simulations to 186 empirical mutualistic networks and show how both direct and indirect effects alter the fitness of species coevolving in these networks. Although the fitness of species usually increased with the number of mutualistic partners, most of the fitness variation across species was driven by indirect effects. We found that these indirect effects prevent coevolving species from adapting to their mutualistic partners and to other sources of selection pressure in the environment, thereby decreasing their fitness. Such decreases are distributed in a predictable way within networks: peripheral species receive more indirect effects and experience higher reductions in fitness than central species. This topological effect was also evident when we analysed an empirical study of an invasion of pollination networks by honeybees. As honeybees became integrated as a central species within networks, they increased the contribution of indirect effects on several other species, reducing their fitness. Our study shows how and why indirect effects can govern the adaptive landscape of species-rich mutualistic assemblages.
Wild animals suppress the spread of socially transmitted misinformation
Despite the benefits of learning about the world through social ties, social connections also provide a conduit for misinformation. Using underwater camera observatories to record the behavior of foraging coral reef fishes, we find that these animals produce and perceive visual motion cues produced by others, thereby forming dynamic information networks. These networks are surprisingly robust to false alarms that occur when one individual flees in the absence of a true shared threat. By reconstructing visual sensory inputs to each animal, we show that this robustness to misinformation about threats inherits from a specific property of their decision-making strategy: dynamic adjustments in sensitivity to socially acquired information. This property can be achieved through a simple and biologically widespread decision-making circuit.
Stable diverse food webs become more common when interactions are more biologically constrained
How complex ecosystems arise and persist has long been a central ecological issue. Attempts to answer this question have typically proceeded by looking at how the percentage of stable assemblages, randomly generated, scales with diversity. Many results generated this way suggest that large stable systems are rarely found in models, in seeming contradiction to the common observation of large complex systems in nature. Here, instead, we use a new inverse approach by assuming that complex systems do exist and characterizing the interactions that would produce this outcome. Finding the kinds of interactions that do produce large complex systems has important implications both for understanding fundamental ecological questions and for understanding the impact of anthropogenic influences on ecological systems.
(Systems) Neuroscience
Learnable latent embeddings for joint behavioural and neural analysis
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural representations1,2,3. In particular, although neural latent embeddings can reveal underlying correlates of behaviour, we lack nonlinear techniques that can explicitly and flexibly leverage joint behaviour and neural data to uncover neural dynamics3,4,5. Here, we fill this gap with a new encoding method, CEBRA, that jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding. We validate its accuracy and demonstrate our tool’s utility for both calcium and electrophysiology datasets, across sensory and motor tasks and in simple or complex behaviours across species. It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free. Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, for the production of consistent latent spaces across two-photon and Neuropixels data, and can provide rapid, high-accuracy decoding of natural videos from visual cortex.
The study links systems neuroscience with cognition and behavior:
Cognition and attention arise from the adaptive coordination of neural systems in response to external and internal demands. The low-dimensional latent subspace that underlies large-scale neural dynamics and the relationships of these dynamics to cognitive and attentional states, however, are unknown. We conducted functional magnetic resonance imaging as human participants performed attention tasks, watched comedy sitcom episodes and an educational documentary, and rested. Whole-brain dynamics traversed a common set of latent states that spanned canonical gradients of functional brain organization, with global desynchronization among functional networks modulating state transitions. Neural state dynamics were synchronized across people during engaging movie watching and aligned to narrative event structures. Neural state dynamics reflected attention fluctuations such that different states indicated engaged attention in task and naturalistic contexts, whereas a common state indicated attention lapses in both contexts. Together, these results demonstrate that traversals along large-scale gradients of human brain organization reflect cognitive and attentional dynamics.
Interestingly, the “entangled” states of distinct subjects, driven by the same trigger of “watching the same show” can be the object of another attention task experiment. Such state can be described by a framework based on network density matrices and described in more detail in this pre-print.
Bio-inspired computing and artificial intelligence
Canonical Computations in Cellular Automata and Their Application for Reservoir Computing
Cellular Automata (CAs) have potential as powerful parallel computational systems, which has lead to the use of CAs as reservoirs in reservoir computing. However, why certain Cellular Automaton (CA) rules, sizes and input encodings are better or worse at a given task is not well understood. We present a method that enables identification and visualization of the specific information content, flow and transformations within the space-time diagram of CA. We interpret each spatio-temporal location in CA’s space-time diagram as a function of its input and call this novel notion the CA’s Canonical Computations (CCs). This allows us to analyze the available information from the space-time diagrams as partitions of the input set. The method also reveals how input-encoder-rule interactions transform the information flow by changing features like spatial and temporal location stability as well as the specific information produced. This general approach for analysing CA is discussed for the engineering of reservoir computing systems.
Climate system
Warning of a forthcoming collapse of the Atlantic meridional overturning circulation
The outcome of this study is concerning, to say the least. An early-warning analysis of the AMOC:
The Atlantic meridional overturning circulation (AMOC) is a major tipping element in the climate system and a future collapse would have severe impacts on the climate in the North Atlantic region. In recent years weakening in circulation has been reported, but assessments by the Intergovernmental Panel on Climate Change (IPCC), based on the Climate Model Intercomparison Project (CMIP) model simulations suggest that a full collapse is unlikely within the 21st century. Tipping to an undesired state in the climate is, however, a growing concern with increasing greenhouse gas concentrations. Predictions based on observations rely on detecting early-warning signals, primarily an increase in variance (loss of resilience) and increased autocorrelation (critical slowing down), which have recently been reported for the AMOC. Here we provide statistical significance and data-driven estimators for the time of tipping. We estimate a collapse of the AMOC to occur around mid-century under the current scenario of future emissions.
Exceeding 1.5°C global warming could trigger multiple climate tipping points
Not less concerning than the previous one:
Climate tipping points occur when change in a part of the climate system becomes self-perpetuating beyond a warming threshold, leading to substantial Earth system impacts. Synthesizing paleoclimate, observational, and model-based studies, we provide a revised shortlist of global “core” tipping elements and regional “impact” tipping elements and their temperature thresholds. Current global warming of ~1.1°C above preindustrial temperatures already lies within the lower end of some tipping point uncertainty ranges. Several tipping points may be triggered in the Paris Agreement range of 1.5 to <2°C global warming, with many more likely at the 2 to 3°C of warming expected on current policy trajectories. This strengthens the evidence base for urgent action to mitigate climate change and to develop improved tipping point risk assessment, early warning capability, and adaptation strategies.
Using cellular automata as the computing core of reserve is perhaps the most direct intersection between complexity science and artificial intelligence. I like this idea.