Behaviour settings, situated action and complexity theory.

In spite of the fact that Roger Barker's groundbreaking research was acclaimed sixty years ago by his contemporaries, it has all been but forgotten among recent generations of psychologists. However, in the wake of developments in dynamical systems and complexity theory, its value for understanding psychological processes in everyday life should be recognized anew. Barker's naturalistic studies of children's daily behaviours in their community revealed that their actions which initially seemed only marginally predictable at the level of individual interaction were, in fact, reliably context-dependent. These results led to the discovery that there are nested structures operating in human habitats as there are throughout the natural world. Barker's discovery of emergent eco-psychological structures, behaviour settings, that are generated from interdependent actions among individuals in the course of everyday life has yet to be fully appreciated because of the continuing dominance of linear, mechanistic models. His recognition of nested systems operating in human habitats is finally coming into its own with the current metatheoretical shift in psychology embracing dynamical models. Additionally, new understanding arises from the consideration of convergent individual developmental histories of situated action and their role in maintaining the historical dimensions of behaviour settings. This article is part of the theme issue 'People, places, things and communities: expanding behaviour settings theory in the twenty-first century'.

Lower-limb coordination changes following a six-week training intervention that elicited enhancements to maximum velocity sprint performance.

Alterations to intra- and inter-limb coordination with improved maximal velocity performance remain largely unexplored. This study quantified within-day variability in lower-limb segmental coordination profiles during maximal velocity sprinting and investigated the modifications to coordination strategies in 15 recreationally active males following a six-week period comprised of a multimodal training programme (intervention group (INT); n=7) or continued participation in sports (control group; n=8). The INT demonstrated a large decrease (effect size=-1.54) in within-day coordination profile variability, suggesting potential skill development. Thigh-thigh coordination modifications for the INT were characterised by an earlier onset of trail thigh reversal in early swing (26 vs. 28% stride) and lead thigh reversal in late swing (76 vs. 79% stride), rather than increases in overall time spent in anti-phase. Moreover, an increase in backward rotation of thigh relative to shank (effect size, 95% CIs: 0.75, 0.17 to 1.33) and shank relative to foot (0.76, -0.17 to 1.68) during late swing likely facilitated more aggressive acceleration of the limb, contributing to reduced touchdown distance and more favourable lower-limb configuration at initial ground contact. These novel findings provide empirical support for the role of longitudinal coordination modifications in improving maximal velocity performance.

Neurodynamical Computing at the Information Boundaries of Intelligent Systems.

Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies-properly conceived as reentrant dynamical flows and not merely as identified groups of neurons-may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.

Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Dynamics and bifurcations in genetic circuits with fibration symmetries.

Circuit building blocks of gene regulatory networks (GRN) have been identified through the fibration symmetries of the underlying biological graph. Here, we analyse analytically six of these circuits that occur as functional and synchronous building blocks in these networks. Of these, the lock-on, toggle switch, Smolen oscillator, feed-forward fibre and Fibonacci fibre circuits occur in living organisms, notably Escherichia coli; the sixth, the repressilator, is a synthetic GRN. We consider synchronous steady states determined by a fibration symmetry (or balanced colouring) and determine analytic conditions for local bifurcation from such states, which can in principle be either steady-state or Hopf bifurcations. We identify conditions that characterize the first bifurcation, the only one that can be stable near the bifurcation point. We model the state of each gene in terms of two variables: mRNA and protein concentration. We consider all possible 'admissible' models-those compatible with the network structure-and then specialize these general results to simple models based on Hill functions and linear degradation. The results systematically classify using graph symmetries the complexity and dynamics of these circuits, which are relevant to understand the functionality of natural and synthetic cells.

Current iliotibial band syndrome alters patterns of running coordination in male and female runners, but not their levels of coordination variability.

The aim of the present study was to compare the coordination patterns and levels of coordination variability of healthy and injured runners with iliotibial band syndrome (ITBS). Sixty runners divided into four groups (15 healthy males, 15 healthy females, 15 males with ITBS and 15 females with ITBS) ran at a steady and freely chosen pace on an over-ground track, and their coordination patterns of the lower limbs were calculated during 10 running stances using the vector coding technique. Both male and female runners with ITBS showed a greater dominance of the pelvis segment and the anti-phase patterns in the frontal plane thigh-pelvis coupling (p = 0.001, η2 = 0.36). In addition, injured female runners showed a greater hip adduction dominance, whereas injured males presented a greater anti-phase pattern in the transverse plane-frontal plane hip coupling (p = 0.003, η2 = 0.08). The levels of coordination variability during running stance did not change between ITBS injured and healthy runners in any of the couplings. Currently injured runners with ITBS appeared to present altered coordination patterns on the hip couplings that were partly dependent on gender but did not lead to changes in the coordination variability levels.

A Mathematical Model for Fibrous Dysplasia: The Role of the Flow of Mutant Cells.

Fibrous dysplasia (FD) is a mosaic non-inheritable genetic disorder of the skeleton in which normal bone is replaced by structurally unsound fibro-osseous tissue. There is no curative treatment for FD, partly because its pathophysiology is not yet fully known. We present a simple mathematical model of the disease incorporating its basic known biology, to gain insight on the dynamics of the involved bone-cell populations, and shed light on its pathophysiology. We develop an analytical study of the model and study its basic properties. The existence and stability of steady states are studied, an analysis of sensitivity on the model parameters is done, and different numerical simulations provide findings in agreement with the analytical results. We discuss the model dynamics match with known facts on the disease, and how some open questions could be addressed using the model.

Emergent perceptual biases from state-space geometry in trained spiking recurrent neural networks.

A stimulus held in working memory is perceived as contracted toward the average stimulus. This contraction bias has been extensively studied in psychophysics, but little is known about its origin from neural activity. By training recurrent networks of spiking neurons to discriminate temporal intervals, we explored the causes of this bias and how behavior relates to population firing activity. We found that the trained networks exhibited animal-like behavior. Various geometric features of neural trajectories in state space encoded warped representations of the durations of the first interval modulated by sensory history. Formulating a normative model, we showed that these representations conveyed a Bayesian estimate of the interval durations, thus relating activity and behavior. Importantly, our findings demonstrate that Bayesian computations already occur during the sensory phase of the first stimulus and persist throughout its maintenance in working memory, until the time of stimulus comparison.

Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene.

A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are customarily based on mechanistic models derived from physical laws of particle movement that are deterministic and do not account for measurement errors inherent in data collection. The resulting analysis compromises on reliably learning about mechanistic factors such as ventilation rates, aerosol generation rates, and filtration efficiencies from field measurements. This manuscript develops a Bayesian state-space modeling framework that synthesizes information from the mechanistic system as well as the field data. We derive a stochastic model from finite difference approximations of differential equations explaining particle concentrations. Our inferential framework trains the mechanistic system using the field measurements from the chamber experiments and delivers reliable estimates of the underlying physical process with fully model-based uncertainty quantification. Our application falls within the realm of the Bayesian "melding" of mechanistic and statistical models and is of significant relevance to environmental hygienists and public health researchers working on assessing the performance of aerosol removal rates for rail car fleets.

Propofol anesthesia destabilizes neural dynamics across cortex.

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is that anesthesia disrupts dynamic stability-the ability of the brain to balance excitability with the need to be stable and controllable. To test this hypothesis, we developed a method for quantifying changes in population-level dynamic stability in complex systems: delayed linear analysis for stability estimation (DeLASE). Propofol was used to transition animals between the awake state and anesthetized unconsciousness. DeLASE was applied to macaque cortex local field potentials (LFPs). We found that neural dynamics were more unstable in unconsciousness compared with the awake state. Cortical trajectories mirrored predictions from destabilized linear systems. We mimicked the effect of propofol in simulated neural networks by increasing inhibitory tone. This in turn destabilized the networks, as observed in the neural data. Our results suggest that anesthesia disrupts dynamical stability that is required for consciousness.

Competition and synergy of Arp2/3 and formins in nucleating actin waves.

Actin assembly and dynamics are crucial for maintaining cell structure and changing physiological states. The broad impact of actin on various cellular processes makes it challenging to dissect the specific role of actin regulatory proteins. Using actin waves that propagate on the cortex of mast cells as a model, we discovered that formins (FMNL1 and mDia3) are recruited before the Arp2/3 complex in actin waves. GTPase Cdc42 interactions drive FMNL1 oscillations, with active Cdc42 and the constitutively active mutant of FMNL1 capable of forming waves on the plasma membrane independently of actin waves. Additionally, the delayed recruitment of Arp2/3 antagonizes FMNL1 and active Cdc42. This antagonism is not due to competition for monomeric actin but rather for their common upstream regulator, active Cdc42, whose levels are negatively regulated by Arp2/3 via SHIP1 recruitment. Collectively, our study highlights the complex feedback loops in the dynamic control of the actin cytoskeletal network.

Exploring the Effects of Instruction and Game Design on Youth Soccer Players' Skill Involvement and Cooperative Team Behaviour.

Objectives: The relationship between task constraints and player behaviors is of interest to coaches tasked with designing practice to optimize learning. This study aims to compare the skill involvements and cooperative team behavior of teams of youth soccer players engaged in a goal exaggeration and/or a prescriptive coach instruction condition compared to a free-play control condition. Methods: Twenty male soccer players aged 12-15 participated in small-sided games under four conditions: free-play, goal exaggeration, prescriptive coach instruction, and combination over four weeks. Using video footage, teams' collective skill involvements (shot, pass, dribble) and passing network characteristics (closeness, density, and betweenness) were measured for each game. Results: A Friedmans rank test identified that playing conditions resulted in significant differences in attempted dribbles (p < .001), goals scored (p < .001), network density (p = .001), closeness (p < .001) and betweenness (p = .002). Teams attempted to dribble the most in the free-play and goal-exaggeration conditions, and the most goals were scored in the goal-exaggeration and combination conditions. Additionally, teams exhibited more well-connected passing networks (i.e. higher density, higher closeness, and lower betweenness values) in the combination condition and higher network density in the explicit instruction condition. Conclusions: The results of this study indicate that coach instruction may be more associated with cooperative team behavior, whereas free-play or manipulating task constraints in the absence of instruction may be associated with players attempting more individual actions.

Temporal information in the anterior cingulate cortex relates to accumulated experiences.

Anterior cingulate cortex (ACC) activity is important for operations that require the ability to integrate multiple experiences over time, such as rule learning, cognitive flexibility, working memory, and long-term memory recall. To shed light on this, we analyzed neuronal activity while rats repeated the same behaviors during hour-long sessions to investigate how activity changed over time. We recorded neuronal ensembles as rats performed a decision-free operant task with varying reward likelihoods at three different response ports (n = 5). Neuronal state space analysis revealed that each repetition of a behavior was distinct, with more recent behaviors more similar than those further apart in time. ACC activity was dominated by a slow, gradual change in low-dimensional representations of neural state space aligning with the pace of behavior. Temporal progression, or drift, was apparent on the top principal component for every session and was driven by the accumulation of experiences and not an internal clock. Notably, these signals were consistent across subjects, allowing us to accurately predict trial numbers based on a model trained on data from a different animal. We observed that non-continuous ramping firing rates over extended durations (tens of minutes) drove the low-dimensional ensemble representations. 40% of ACC neurons' firing ramped over a range of trial lengths and combinations of shorter duration ramping neurons created ensembles that tracked longer durations. These findings provide valuable insights into how the ACC, at an ensemble level, conveys temporal information by reflecting the accumulation of experiences over extended periods.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.

Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

Similarity in evoked responses does not imply similarity in macroscopic network states.

It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N = 242). Participants performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.

As a dynamical system, the brain can encode information at the module (e.g., brain regions) or the network level (e.g., connections between brain regions). This means that two tasks can produce the same pattern of activation, but differ in their network profile. Here we tested this using two tasks with largely similar cognitive requirements. Despite producing nearly identical macroscopic activation patterns, the two tasks produced different functional network profiles. These findings confirm prior theoretical work that similarity in task activation does not imply the same similarity in underlying network states.

Exploring Simplicity Bias in 1D Dynamical Systems.

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability-complexity relationships may be a useful tool when studying patterns in dynamical systems.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation.

Macromolecular complexes are often composed of diverse subunits. The self-assembly of these subunits is inherently nonequilibrium and must avoid kinetic traps to achieve high yield over feasible timescales. We show how the kinetics of self-assembly benefits from diversity in subunits because it generates an expansive parameter space that naturally improves the "expressivity" of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched the parameter spaces of mass-action kinetic models to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate-constants or external and active control of subunits to efficiently assemble. Internal design of a hierarchy of subunit binding rates generates self-assembly that can robustly avoid kinetic traps for all concentrations and energetics, but it places strict constraints on selection of relative rates. External control via subunit titration is more versatile, avoiding kinetic traps for any system without requiring molecular engineering of binding rates, albeit less efficiently and robustly. We derive theoretical expressions for the timescales of kinetic traps, and we demonstrate our optimization method applies not just for design but inference, extracting intersubunit binding rates from observations of yield-vs.-time for a heterotetramer. Overall, we identify optimal kinetic protocols for self-assembly as a powerful mechanism to achieve efficient and high-yield assembly in synthetic systems whether robustness or ease of "designability" is preferred.

Time-dependent ligand-receptor binding kinetics and functionality in a heterodimeric receptor model.

GPCRs heteromerize both in CNS and non-CNS regions. The cell uses receptor heteromerization to modulate receptor functionality and to provide fine tuning of receptor signaling. In order for pharmacologists to explore these mechanisms for therapeutic purposes, quantitative receptor models are needed. We have developed a time-dependent model of the binding kinetics and functionality of a preformed heterodimeric receptor involving two drugs. Two cases were considered: both or only one of the drugs are in excess with respect to the total concentration of the receptor. The latter case can be applied to those situations in which a drug causes unwanted side effects that need to be reduced by decreasing its concentration. The required efficacy can be maintained by the allosteric effects mutually exerted by the two drugs in the two-drug combination system. We discuss this concept assuming that the drug causing unwanted side effects is an opioid and that analgesia is the therapeutic effect. As additional points, allosteric modulation by endogenous compounds and synthetic bivalent ligands was included in the study. Receptor heteromerization offers a mechanistic understanding and quantification of the pharmacological effects elicited by combinations of two drugs at different doses and with different efficacies and cooperativity effects, thus providing a conceptual framework for drug combination therapy.

Generation of genetic codes with 2-adic codon algebra and adaptive dynamics.

This is a brief review on modeling genetic codes with the aid of 2-adic dynamical systems. In this model amino acids are encoded by the attractors of such dynamical systems. Each genetic code is coupled to the special class of 2-adic dynamics. We consider the discrete dynamical systems, These are the iterations of a function F:Z2→Z2, where Z2 is the ring of 2-adic numbers (2-adic tree). A genetic code is characterized by the set of attractors of a function belonging to the code generating functional class. The main mathematical problem is to reduce degeneration of dynamic representation and select the optimal generating function. Here optimality can be treated in many ways. One possibility is to consider the Lipschitz functions playing the crucial role in general theory of iterations. Then we minimize the Lip-constant. The main issue is to find the proper biological interpretation of code-functions. One can speculate that the evolution of the genetic codes can be described in information space of the nucleotide-strings endowed with ultrametric (treelike) geometry. A code-function is a fitness function; the solutions of the genetic code optimization problem are attractors of the code-function. We illustrate this approach by generation of the standard nuclear and (vertebrate) mitochondrial genetics codes.

Recurrent flow patterns as a basis for two-dimensional turbulence: Predicting statistics from structures.

A dynamical systems approach to turbulence envisions the flow as a trajectory through a high-dimensional state space [Hopf, Commun. Appl. Maths 1, 303 (1948)]. The chaotic dynamics are shaped by the unstable simple invariant solutions populating the inertial manifold. The hope has been to turn this picture into a predictive framework where the statistics of the flow follow from a weighted sum of the statistics of each simple invariant solution. Two outstanding obstacles have prevented this goal from being achieved: 1) paucity of known solutions and 2) the lack of a rational theory for predicting the required weights. Here, we describe a method to substantially solve these problems, and thereby provide compelling evidence that the probability density functions (PDFs) of a fully developed turbulent flow can be reconstructed with a set of unstable periodic orbits. Our method for finding solutions uses automatic differentiation, with high-quality guesses constructed by minimizing a trajectory-dependent loss function. We use this approach to find hundreds of solutions in turbulent, two-dimensional Kolmogorov flow. Robust statistical predictions are then computed by learning weights after converting a turbulent trajectory into a Markov chain for which the states are individual solutions, and the nearest solution to a given snapshot is determined using a deep convolutional autoencoder. In this study, the PDFs of a spatiotemporally chaotic system have been successfully reproduced with a set of simple invariant states, and we provide a fascinating connection between self-sustaining dynamical processes and the more well-known statistical properties of turbulence.