Complexity Thoughts: Issue #53
Unraveling complexity: building knowledge, one paper at a time
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Foundations of network science and complex systems
Classifying Two-Body Hamiltonians for Quantum Darwinism
It’s fascinating, and remarkably puzzling, that our classical understanding of measurement works so seamlessly to explain the everyday world. This is especially surprising given that the Universe, at its core, operates according to the strange and counterintuitive rules of quantum mechanics. Somehow, classical reality (the stable, predictable world we interact with) emerges from this quantum foundation. But how? What’s the mechanism behind this emergence?
One of the most well-developed frameworks to tackle this question is quantum Darwinism. At its heart is the concept of objectivity, a key feature of classical reality. Objectivity means that multiple observers can independently measure a system and agree on its properties. In classical physics, this seems obvious, but in the quantum world, however, things are very different: measurements are indirect, often disturb the system, and are anything but straightforward.
Quantum Darwinism suggests that objectivity emerges from a system’s interaction with its environment. When a quantum system interacts with its surroundings, information about the system gets imprinted into the environment, and not just once. This encoding happens with massive redundancy, allowing multiple observers to independently access the same information. The result? A shared, objective view of the system—a crucial step in the transition from the quantum to the classical.
But here’s the catch: while quantum Darwinism is an elegant framework, an important open question has lingered: what kinds of quantum systems actually support this emergence of classical objectivity? This is where this paper steps in: by addressing this question, the authors bring clarity to a fundamental piece of the puzzle connecting the quantum and classical worlds.
Quantum Darwinism is a paradigm to understand how classically objective reality emerges from within a fundamentally quantum universe. Despite the growing attention that this field of research has been enjoying, it is currently not known what specific properties a given Hamiltonian describing a generic quantum system must have to allow the emergence of classicality. Therefore, in the present work, we consider a broadly applicable generic model of an arbitrary finite-dimensional system interacting with an environment formed from an arbitrary collection of finite-dimensional degrees of freedom via an unspecified, potentially time-dependent Hamiltonian containing at most two-body interaction terms. We show that such models support quantum Darwinism if the set of operators acting on the system which enter the Hamiltonian satisfy a set of commutation relations with a pointer observable and with one other. We demonstrate our results by analyzing a wide range of example systems: a qutrit interacting with a qubit environment, a qubit-qubit model with interactions alternating in time, and a series of collision models including a minimal model of a quantum Maxwell demon.
Evolution & Ecosystems
Unifying Research on Social–Ecological Resilience and Collapse
How can we rigorously quantify identity and collapse? What are the relationships between structure, process, and function in social–ecological systems? Is small-scale collapse essential for long-term persistence? How can analyses of collapses of past human societies and ecological communities be extended to better interpret the risks and opportunities of collapse in the Anthropocene?
As social–ecological systems enter a period of rapid global change, science must predict and explain ‘unthinkable’ social, ecological, and social–ecological collapses. Existing theories of collapse are weakly integrated with resilience theory and ideas about vulnerability and sustainability.
Mechanisms of collapse are poorly understood and often heavily contested. Progress in understanding collapse requires greater clarity on system identity and alternative causes of collapse. Archaeological theories have focused on a limited range of reasons for system collapse. In resilience theory, the adaptive cycle has been used to describe collapse but offers little insight into the mechanisms that cause it.
Theories of collapse should connect structure and process. Mechanistic, structure–process–function theories of collapse suggest new avenues for understanding and improving resilience.
Ecosystems influence human societies, leading people to manage ecosystems for human benefit. Poor environmental management can lead to reduced ecological resilience and social–ecological collapse. We review research on resilience and collapse across different systems and propose a unifying social–ecological framework based on (i) a clear definition of system identity; (ii) the use of quantitative thresholds to define collapse; (iii) relating collapse processes to system structure; and (iv) explicit comparison of alternative hypotheses and models of collapse. Analysis of 17 representative cases identified 14 mechanisms, in five classes, that explain social–ecological collapse. System structure influences the kind of collapse a system may experience. Mechanistic theories of collapse that unite structure and process can make fundamental contributions to solving global environmental problems.
Biological Systems
The Human Cell Atlas: towards a first draft atlas
This is huge!
Established in 2016, the Human Cell Atlas (HCA) consortium set out to create a comprehensive biological map of cells within the human body. Now progressing into a data integration phase, the HCA is working towards assembling the first draft of this atlas, focusing on 18 biological network atlases. Towards this milestone, they have compiled a collection of papers that highlight essential achievements of this new stage.
The HCA data portal currently hosts data from approximately 62 million cells collected from around 9,100 donors. To facilitate data integration, the consortium is constructing 18 HCA Biological Network Atlases, as shown in this diagram. Each network consolidates all available HCA data related to individual tissues or organs. To date, draft atlases from three networks — lung, nervous system and eye — have been assembled by HCA researchers collaborating globally with other consortia. The papers in this collection represent significant progress in assembling these Biological Network Atlases.
A three-node Turing gene circuit forms periodic spatial patterns in bacteria
Turing patterns are self-organizing systems that can form spots, stripes, or labyrinths. Proposed examples in tissue organization include zebrafish pigmentation, digit spacing, and many others. The theory of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters. This has complicated the engineering of synthetic Turing gene circuits from first principles, although natural genetic Turing networks have been identified. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness. The system reproducibly generated stationary, periodic, concentric stripe patterns in growing E. coli colonies. A partial differential equation model reproduced the patterns, with a Turing parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to nanotechnologies, such as patterned biomaterial deposition, and provide insights into developmental patterning programs.
Neuroscience
Understanding the molecular diversity of synapses
Synapses are composed of thousands of proteins, providing the potential for extensive molecular diversity to shape synapse type-specific functional specializations. In this Review, we explore the landscape of synaptic diversity and describe the mechanisms that expand the molecular complexity of synapses, from the genotype to the regulation of gene expression to the production of specific proteoforms and the formation of localized protein complexes. We emphasize the importance of examining every molecular layer and adopting a systems perspective to understand how these interconnected mechanisms shape the diverse functional and structural properties of synapses. We explore current frameworks for classifying synapses and methodologies for investigating different synapse types at varying scales, from synapse-type-specific proteomics to advanced imaging techniques with single-synapse resolution. We highlight the potential of synapse-type-specific approaches for integrating molecular data with cellular functions, circuit organization and organismal phenotypes to enable a more holistic exploration of neuronal phenomena across different scales.
Heterogeneous patterns of brain atrophy in schizophrenia localize to a common brain network
Understanding the neuroanatomy of schizophrenia remains elusive due to heterogeneous findings across neuroimaging studies. Here we investigated whether patterns of brain atrophy associated with schizophrenia would localize to a common brain network using a coordinate network mapping meta-analysis approach. Utilizing the human connectome as a wiring diagram, we identified a connectivity pattern, a schizophrenia network, uniting heterogeneous results from 90 published studies of atrophy in schizophrenia (total n > 8,000). This network was specific to schizophrenia, differentiating it from atrophy in individuals at high risk for psychosis (n = 3,038), normal aging (n = 4,195), neurodegenerative disorders (n = 3,707) and other psychiatric conditions (n = 3,432). The network was also stable with disease progression and across different clusters of schizophrenia symptoms. Patterns of brain atrophy in schizophrenia were negatively correlated with lesions linked to psychosis-related thought processes in an independent cohort (n = 181). Our results propose a unique, stable, and unified schizophrenia network, addressing a significant portion of the heterogeneity observed in previous atrophy studies.
The unbearable slowness of being: Why do we live at 10 bits/s?
This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ∼10^9 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the “outer” brain handles fast high-dimensional sensory and motor signals, whereas the “inner” brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.
Simulation and assimilation of the digital human brain
Here we present the Digital Brain (DB)—a platform for simulating spiking neuronal networks at the large neuron scale of the human brain on the basis of personalized magnetic resonance imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs—including a two-level routing scheme between GPUs to accelerate spike transmission in up to 47.8 trillion neuronal synapses—was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications.
Correlated signatures of social behavior in cerebellum and anterior cingulate cortex
Social behaviour is important for many animals, especially humans. It governs interactions between individuals and groups. One of the regions involved in social behaviour is the cerebellum, a part of the brain commonly known for controlling movement. It is likely that the cerebellum connects and influences other socially important areas in the brain, such as the anterior cingulate cortex. How exactly these regions communicate during social interaction is not well understood.
One of the challenges studying communication between areas in the brain has been a lack of tools that can measure neural activity in multiple regions at once. To address this problem, Hur et al. developed a device called the E-Scope. The E-Scope can measure brain activity from two places in the brain at the same time. It can simultaneously record imaging and electrophysiological data of the different neurons. It is also small enough to be attached to animals without inhibiting their movements.
Hur et al. tested the E-Scope by studying neurons in two regions of the cerebellum, called the right Crus I and the dentate nucleus, and in the anterior cingulate cortex during social interactions in mice. The E-Scope recorded from the animals as they interacted with other mice and compared them with those in mice that interacted with objects.
During social interactions, Purkinje cells in the right Crus I were mostly less active, while neurons in the dentate nucleus and anterior cingulate cortex became overall more active. These results suggest that communication between the cerebellum and the anterior cingulate cortex is an important part of how the mouse brain coordinates social behaviour.
The study of Hur et al. deepens our understanding of the function of the cerebellum in social behaviour. The E-Scope is an openly available tool to allow researchers to record communication between remote brain areas in small animals. This could be important to researchers trying to understand conditions like autism, which can involve difficulties in social interaction, or injuries to the cerebellum resulting in personality changes.
The cerebellum has been implicated in the regulation of social behavior. Its influence is thought to arise from communication, via the thalamus, to forebrain regions integral in the expression of social interactions, including the anterior cingulate cortex (ACC). However, the signals encoded or the nature of the communication between the cerebellum and these brain regions is poorly understood. Here, we describe an approach that overcomes technical challenges in exploring the coordination of distant brain regions at high temporal and spatial resolution during social behavior. We developed the E-Scope, an electrophysiology-integrated miniature microscope, to synchronously measure extracellular electrical activity in the cerebellum along with calcium imaging of the ACC. This single coaxial cable device combined these data streams to provide a powerful tool to monitor the activity of distant brain regions in freely behaving animals. During social behavior, we recorded the spike timing of multiple single units in cerebellar right Crus I (RCrus I) Purkinje cells (PCs) or dentate nucleus (DN) neurons while synchronously imaging calcium transients in contralateral ACC neurons. We found that during social interactions a significant subpopulation of cerebellar PCs were robustly inhibited, while most modulated neurons in the DN were activated, and their activity was correlated with positively modulated ACC neurons. These distinctions largely disappeared when only non-social epochs were analyzed suggesting that cerebellar-cortical interactions were behaviorally specific. Our work provides new insights into the complexity of cerebellar activation and co-modulation of the ACC during social behavior and a valuable open-source tool for simultaneous, multimodal recordings in freely behaving mice.
Human Behavior
Behaviour-based dependency networks between places shape urban economic resilience
Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks.