Complexity Thoughts: Issue #31
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
Ecosystems
our analyses identified two alternative ways in which global drylands respond to increasing abiotic stress through self-organization
Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.
we empirically confirm that the interaction network structure of plant–pollinator communities can explain their temporal dynamics across levels of biological organization, a long-lasting open question in ecology
Despite clear evidence that some pollinator populations are declining, our ability to predict pollinator communities prone to collapse or species at risk of local extinction is remarkably poor. Here, we develop a model grounded in the structuralist approach that allows us to draw sound predictions regarding the temporal persistence of species in mutualistic networks. Using high-resolution data from a six-year study following 12 independent plant–pollinator communities, we confirm that pollinator species with more persistent populations in the field are theoretically predicted to tolerate a larger range of environmental changes. Persistent communities are not necessarily more diverse, but are generally located in larger habitat patches, and present a distinctive combination of generalist and specialist species resulting in a more nested structure, as predicted by previous theoretical work. Hence, pollinator interactions directly inform about their ability to persist, opening the door to use theoretically informed models to predict species’ fate within the ongoing global change.
Biological Systems
Active Spaghetti: Collective Organization in Cyanobacteria
Cyanobacteria were among the first photosynthetic organisms on Earth, with a crucial role in Earth's ecological and evolutionary dynamics. Using photosynthesis to produce oxygen, they contributed to transform the Earth's atmosphere, enabling the evolution of aerobic life forms during the Great Oxygenation Event (a possible consequence of ecological dynamics modulated by planetary change, according to this study). These organisms form the foundation of many aquatic ecosystems, acting as primary producers supporting diverse food webs, with their ability to fix atmospheric nitrogen to enrich ecosystems by converting nitrogen into a usable form for other organisms. Since they are involved in key biogeochemical cycles (e.g., carbon and oxygen) they play a crucial role in sustaining environmental stability and, according to some recent studies, they could be included in synthetic circuit designs for earth terraformation.
In physics, they can be studied as active matter, describing out-of-equilibrium systems, and the following paper helps to understand the emergence of multicellular filamentary structures, providing some hints about how evolutionary strategies might depend on collective action.
Filamentous cyanobacteria can show fascinating examples of nonequilibrium self-organization, which, however, are not well understood from a physical perspective. We investigate the motility and collective organization of colonies of these simple multicellular lifeforms. As their area density increases, linear chains of cells gliding on a substrate show a transition from an isotropic distribution to bundles of filaments arranged in a reticulate pattern. Based on our experimental observations of individual behavior and pairwise interactions, we introduce a nonreciprocal model accounting for the filaments’ large aspect ratio, fluctuations in curvature, motility, and nematic interactions. This minimal model of active filaments recapitulates the observations, and rationalizes the appearance of a characteristic length scale in the system, based on the Péclet number of the cyanobacteria filaments.
Structure is beauty, but not always truth
Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and “anti-targets.” Overcoming these challenges will amplify the impact of structural biology on drug discovery.
Neuroscience
A thalamocortical substrate for integrated information via critical synchronous bursting
Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal–apical and basal–somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.
Exploration-Exploitation Paradigm for Networked Biological Systems
The stochastic exploration of the configuration space and the exploitation of functional states underlie many biological processes. The evolutionary dynamics stands out as a remarkable example. Here, we introduce a novel formalism that mimics evolution and encodes a general exploration-exploitation dynamics for biological networks. We apply it to the brain wiring problem, focusing on the maturation of that of the nematode Caenorhabditis elegans. We demonstrate that a parsimonious maxent description of the adult brain combined with our framework is able to track down the entire developmental trajectory.
The feasibility of artificial consciousness through the lens of neuroscience
I am glad that this paper, from the neuroscience community, argues that it is difficult to defend the position that LLMs like chatGPT are — or will be soon — conscious. I have written two short essays about this:
For more arguments, just read the paper:
Interactions with large language models (LLMs) have led to the suggestion that these models may soon be conscious. From the perspective of neuroscience, this position is difficult to defend. For one, the inputs to LLMs lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architectures of present-day artificial intelligence algorithms are missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today. The existence of living organisms depends on their actions and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness.