Complexity Thoughts: Issue #32

Unraveling complexity: building knowledge, one paper at a time

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Dall-e 3 representation of this issue’s content

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Foundations of network science and complex systems

Predicting multiple observations in complex systems through low-dimensional embeddings

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

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Social behavior

Two really interesting papers here. We usually think that cumulative culture — ie, the progressive accumulation of knowledge and enhancement of skills/technologies following (or triggering) the spread of innovations — is a human phenomenon only.

It seems that it is not the case! Bumblebees and chimpanzees exhibit similar behavior (read this News&Views for a summary and more context).

It has been argued that human culture rests on a unique ability to learn from others more than we could possibly learn alone in a lifetime. Two studies show that we share this ability with bumblebees and chimpanzees

Bumblebees socially learn behaviour too complex to innovate alone

Culture refers to behaviours that are socially learned and persist within a population over time. Increasing evidence suggests that animal culture can, like human culture, be cumulative: characterized by sequential innovations that build on previous ones¹. However, human cumulative culture involves behaviours so complex that they lie beyond the capacity of any individual to independently discover during their lifetime^1,2,3. To our knowledge, no study has so far demonstrated this phenomenon in an invertebrate. Here we show that bumblebees can learn from trained demonstrator bees to open a novel two-step puzzle box to obtain food rewards, even though they fail to do so independently. Experimenters were unable to train demonstrator bees to perform the unrewarded first step without providing a temporary reward linked to this action, which was removed during later stages of training. However, a third of naive observer bees learned to open the two-step box from these demonstrators, without ever being rewarded after the first step. This suggests that social learning might permit the acquisition of behaviours too complex to ‘re-innovate’ through individual learning. Furthermore, naive bees failed to open the box despite extended exposure for up to 24 days. This finding challenges a common opinion in the field: that the capacity to socially learn behaviours that cannot be innovated through individual trial and error is unique to humans.

Chimpanzees use social information to acquire a skill they fail to innovate

Cumulative cultural evolution has been claimed to be a uniquely human phenomenon pivotal to the biological success of our species. One plausible condition for cumulative cultural evolution to emerge is individuals’ ability to use social learning to acquire know-how that they cannot easily innovate by themselves. It has been suggested that chimpanzees may be capable of such know-how social learning, but this assertion remains largely untested. Here we show that chimpanzees use social learning to acquire a skill that they failed to independently innovate. By teaching chimpanzees how to solve a sequential task (one chimpanzee in each of the two tested groups, n = 66) and using network-based diffusion analysis, we found that 14 naive chimpanzees learned to operate a puzzle box that they failed to operate during the preceding three months of exposure to all necessary materials. In conjunction, we present evidence for the hypothesis that social learning in chimpanzees is necessary and sufficient to acquire a new, complex skill after the initial innovation.

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Evolution

Empirical fitness landscapes reveal accessible evolutionary paths

That only a few paths are favoured also implies that evolution might be more reproducible than is commonly perceived, or even be predictable. It is important to note that evolutionary speed and predictability are not determined only by molecular constraints, but also by population dynamics

When attempting to understand evolution, we traditionally rely on analysing evolutionary outcomes, despite the fact that unseen intermediates determine its course. A handful of recent studies has begun to explore these intermediate evolutionary forms, which can be reconstructed in the laboratory. With this first view on empirical evolutionary landscapes, we can now finally start asking why particular evolutionary paths are taken.

Ecosystems

The collapse of cooperation during range expansion of Pseudomonas aeruginosa

When put into an environment where they have to cooperate to grow, the bacteria became more vulnerable to cheaters. It's a phenomenon that happens in industrious human societies as well, but it's a risk we all have to take to maximize our growth. — Lingchong You

Cooperation is commonly believed to be favourable in spatially structured environments, as these systems promote genetic relatedness that reduces the likelihood of exploitation by cheaters. Here we show that a Pseudomonas aeruginosa population that exhibited cooperative swarming was invaded by cheaters when subjected to experimental evolution through cycles of range expansion on solid media, but not in well-mixed liquid cultures. Our results suggest that cooperation is disfavoured in a more structured environment, which is the opposite of the prevailing view. We show that spatial expansion of the population prolongs cooperative swarming, which was vulnerable to cheating. Our findings reveal a mechanism by which spatial structures can suppress cooperation through modulation of the quantitative traits of cooperation, a process that leads to population divergence towards distinct colonization strategies.

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Neuroscience

The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity

Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant—though often neglected—contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>10⁷ simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.

Later the authors introduce their adaptive stochasticity hypothesis:

heightened stochasticity within the developing brain may serve as an adaptive mechanism in situations of environmental uncertainty. In other words, it could constitute an active response that the brain implements in stressful or uncertain environments, potentially through upregulation of cellular stochastic processes. This would mirror demonstrations that neuronal noise can itself be an important source of information within developing neural systems, as it allows synapses to better communicate their degree of uncertainty

Functional brain networks reflect spatial and temporal autocorrelation

A paper showing that some complex network measures widely used for the analysis of connectomes are closely related to spatial and temporal autocorrelations: at the level that their variation across subjects could be predicted almost entirely by spatial and temporal autocorrelations. See this News & Views.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

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Oldies but Goldies

Adaptation and the Form-Function Complex

Walter J. Bock and Gerd von Wahlert, in 1965, writing about the form-function complex.

They emphasize the inseparability of form and function to understand biological adaptation, and differentiate between the environment that organisms actually interact with (umwelt) and the broader potential environment (umgebung).
Concerning adaptation they also distinguish between physiological (or somatic) adaptation and evolutionary adaptation: the organism-environment synergy is the key to biological adaptation.

They explicitly mention the form-function complex to stress the inseparability of an organism's form (its physical attributes) and function (the actions derived from these attributes). Since each aspect influences the other in the organism's evolutionary trajectory, understanding adaptation to environment (the organism-environment bond) requires a combined analysis of its form-function complex , since organisms follow selective pressures and evolutionary pathways.

I can’t fully grasp the difference between universal and evolutionary adaptation, still. On the one hand, they consider universal adaptation, i.e. the inherent compatibility of life with its environment. On the other hand they consider evolutionary adaptation, which focuses on the hereditary adjustments of species to specific environmental challenges.
It looks like that universal adaptation is a kind of (static?) baseline not at all related to evolutionary pathways: if the environmental conditions change and life forms cannot adjust accordingly (by means of evolution?), they risk extinction. Evolutionary adaptation is a dynamic process, not a static one.

But still I don’t see how it is possible to define adaptation in static terms. Ideas?