A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

Distributed control for geometric pattern formation of large-scale multirobot systems

Introduction: Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarms of drones or robots, and smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly. Methods and result: In this paper, we provide a distributed displacement-based control law that allows large groups of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility. Results: We show the validity and robustness of our approach via numerical simulations and experiments, comparing it, where possible, with other approaches from the existing literature.

Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.

Cooperative mechanics of PR65 scaffold underlies the allosteric regulation of the phosphatase PP2A

PR65, a horseshoe-shaped scaffold composed of 15 HEAT (observed in Huntingtin, elongation factor 3, protein phosphatase 2A, and the yeast kinase TOR1) repeats, forms, together with catalytic and regulatory subunits, the heterotrimeric protein phosphatase PP2A. We examined the role of PR65 in enabling PP2A enzymatic activity with computations at various levels of complexity, including hybrid approaches that combine full-atomic and elastic network models. Our study points to the high flexibility of this scaffold allowing for end-to-end distance fluctuations of 40-50 Å between compact and extended conformations. Notably, the intrinsic dynamics of PR65 facilitates complexation with the catalytic subunit and is retained in the PP2A complex enabling PR65 to engage the two domains of the catalytic subunit and provide the mechanical framework for enzymatic activity, with support from the regulatory subunit. In particular, the intra-repeat coils at the C-terminal arm play an important role in allosterically mediating the collective dynamics of PP2A, pointing to target sites for modulating PR65 function.

Spatial distribution of heterogeneity as a modulator of collective dynamics in pancreatic beta-cell networks and beyond

We study the impact of spatial distribution of heterogeneity on collective dynamics in gap-junction coupled beta-cell networks comprised on cells from two populations that differ in their intrinsic excitability. Initially, these populations are uniformly and randomly distributed throughout the networks. We develop and apply an iterative algorithm for perturbing the arrangement of the network such that cells from the same population are increasingly likely to be adjacent to one another. We find that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity decreases as network sortedness increases. Moreover, for weak coupling, we find that regimes of partial synchronisation and wave propagation arise, which depend both on network drive and network sortedness. We then demonstrate the utility of this algorithm for studying the distribution of heterogeneity in general networks, for which we use Watts-Strogatz networks as a case study. This work highlights the importance of heterogeneity in node dynamics in establishing collective rhythms in complex, excitable networks and has implications for a wide range of real-world systems that exhibit such heterogeneity.

How environment affects active particle swarms: a case study

We investigate the collective motion of self-propelled agents in an environment filled with obstacles that are tethered to fixed positions via springs. The active particles are able to modify the environment by moving the obstacles through repulsion forces. This creates feedback interactions between the particles and the obstacles from which a breadth of patterns emerges (trails, band, clusters, honey-comb structures, etc.). We will focus on a discrete model first introduced in Aceves-Sanchez P et al. (2020, Bull. Math. Biol. 82, 125 (doi:10.1007/s11538-020-00805-z)), and derived into a continuum PDE model. As a first major novelty, we perform an in-depth investigation of pattern formation of the discrete and continuum models in two dimensions: we provide phase-diagrams and determine the key mechanisms for bifurcations to happen using linear stability analysis. As a result, we discover that the agent-agent repulsion, the agent-obstacle repulsion and the obstacle's spring stiffness are the key forces in the appearance of patterns, while alignment forces between the particles play a secondary role. The second major novelty lies in the development of an innovative methodology to compare discrete and continuum models that we apply here to perform an in-depth analysis of the agreement between the discrete and continuum models.

Biological benefits of collective swimming of sperm in a viscoelastic fluid

Collective swimming is evident in the sperm of several mammalian species. In bull (Bos taurus) sperm, high viscoelasticity of the surrounding fluid induces the sperm to form dynamic clusters. Sperm within the clusters swim closely together and align in the same direction, yet the clusters are dynamic because individual sperm swim into and out of them over time. As the fluid in part of the mammalian female reproductive tract contains mucus and, consequently, is highly viscoelastic, this mechanistic clustering likely happens in vivo. Nevertheless, it has been unclear whether clustering could provide any biological benefit. Here, using a microfluidic in vitro model with viscoelastic fluid, we found that the collective swimming of bull sperm in dynamic clusters provides specific biological benefits. In static viscoelastic fluid, clustering allowed sperm to swim in a more progressive manner. When the fluid was made to flow in the range of 2.43-4.05 1/sec shear rate, clustering enhanced the ability of sperm to swim upstream. We also found that the swimming characteristics of sperm in our viscoelastic fluid could not be fully explained by the hydrodynamic model that has been developed for sperm swimming in a low-viscosity, Newtonian fluid. Overall, we found that clustered sperm swam more oriented with each other in the absence of flow, were able to swim upstream under intermediate flows, and better withstood a strong flow than individual sperm. Our results indicate that the clustering of sperm can be beneficial to sperm migrating against an opposing flow of viscoelastic fluid within the female reproductive tract.

Non-local interactions in collective motion

The collective motion of animal groups often exhibits velocity-velocity correlations between nearest neighbours, with the strongest velocity correlations observed at the shortest inter-animal spacings. This may have been a motivational factor in the development of models based primarily on short-ranged interactions. Here we ask whether such observations necessarily mean that the interactions are short-ranged. We develop a minimal model of collective motion capable of supporting interactions of arbitrary range and show that it represents a counterexample: the strongest velocity correlations emerge at the shortest distances, even when the interactions are explicitly non-local.

Collective dynamics of sperm cells

While only a single sperm may fertilize the egg, getting to the egg can be facilitated, and possibly enhanced, by sperm group dynamics. Examples range from the trains formed by wood mouse sperm to the bundles exhibited by echidna sperm. In addition, observations of wave-like patterns exhibited by ram semen are used to score prospective sample fertility for artificial insemination in agriculture. In this review, we discuss these experimental observations of collective dynamics, as well as describe recent mechanistic models that link the motion of individual sperm cells and their flagella to observed collective dynamics. Establishing this link in models involves negotiating the disparate time- and length scales involved, typically separated by a factor of 1000, to capture the dynamics at the greatest length scales affected by mechanisms at the shortest time scales. Finally, we provide some outlook on the subject, in particular, the open questions regarding how collective dynamics impacts fertility. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Higher-Order Cumulants Drive Neuronal Activity Patterns, Inducing UP-DOWN States in Neural Populations

A major challenge in neuroscience is to understand the role of the higher-order correlations structure of neuronal populations. The dichotomized Gaussian model (DG) generates spike trains by means of thresholding a multivariate Gaussian random variable. The DG inputs are Gaussian distributed, and thus have no interactions beyond the second order in their inputs; however, they can induce higher-order correlations in the outputs. We propose a combination of analytical and numerical techniques to estimate higher-order, above the second, cumulants of the firing probability distributions. Our findings show that a large amount of pairwise interactions in the inputs can induce the system into two possible regimes, one with low activity (“DOWN state”) and another one with high activity (“UP state”), and the appearance of these states is due to a combination between the third- and fourth-order cumulant. This could be part of a mechanism that would help the neural code to upgrade specific information about the stimuli, motivating us to examine the behavior of the critical fluctuations through the Binder cumulant close to the critical point. We show, using the Binder cumulant, that higher-order correlations in the outputs generate a critical neural system that portrays a second-order phase transition.

Network architectures supporting learnability

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Black soldier fly larvae feed by forming a fountain around food

The black soldier fly is a non-pest insect of interest to the sustainability community due to the high eating rates of its edible larvae. When found on carcases or piles of rotting fruit, this larva often outcompetes other species of scavengers for food. In this combined experimental and theoretical study, we elucidate the mechanism by which groups of black soldier fly larvae can eat so quickly. We use time-lapse videography and particle image velocimetry to investigate feeding by black soldier fly larvae. Individually, larvae eat in 5 min bursts, for 44% of the time, they are near food. This results in their forming roadblocks around the food, reducing the rate that food is consumed. To overcome these limitations, larvae push each other away from the food source, resulting in the formation of a fountain of larvae. Larvae crawl towards the food from below, feed and then are expelled on the top layer. This self-propagating flow pushes away potential roadblocks, thereby increasing eating rate. We present mathematical models for the rate of eating, incorporating flow rates measured from our experiments.

From flagellar undulations to collective motion: predicting the dynamics of sperm suspensions

Swimming cells and microorganisms are as diverse in their collective dynamics as they are in their individual shapes and propulsion mechanisms. Even for sperm cells, which have a stereotyped shape consisting of a cell body connected to a flexible flagellum, a wide range of collective dynamics is observed spanning from the formation of tightly packed groups to the display of larger-scale, turbulence-like motion. Using a detailed mathematical model that resolves flagellum dynamics, we perform simulations of sperm suspensions containing up to 1000 cells and explore the connection between individual and collective dynamics. We find that depending on the level of variation in individual dynamics from one swimmer to another, the sperm exhibit either a strong tendency to aggregate, or the suspension exhibits large-scale swirling. Hydrodynamic interactions govern the formation and evolution of both states. In addition, a quantitative analysis of the states reveals that the flows generated at the time scale of flagellum undulations contribute significantly to the overall energy in the surrounding fluid, highlighting the importance of resolving these flows.

Co-doped MnFe2O4 nanoparticles: magnetic anisotropy and interparticle interactions

The effect of cobalt doping on the magnetic properties of Mn_{1- x} Co _x Fe₂O₄ nanoparticles was investigated. All samples consist of ensembles of nanoparticles with a spherical shape and average diameter of about 10 nm, showing small structural changes due to the substitution. Besides having the same morpho-structural properties, the effect of the chemical composition, i.e., the amount of Co doping, produces marked differences on the magnetic properties, especially on the magnetic anisotropy, with evident large changes in the coercive field. Moreover, Co substitution has a profound effect on the interparticle interactions, too. A dipolar-based interaction regime is detected for all samples; in addition, the intensity of the interactions shows a possible relation with the single particle anisotropy. Finally, the sample with the strongest interaction regime shows a superspin glass state confirmed by memory effect dynamics.

Self-Organized Nuclear Positioning Synchronizes the Cell Cycle in Drosophila Embryos

The synchronous cleavage divisions of early embryogenesis require coordination of the cell-cycle oscillator, the dynamics of the cytoskeleton, and the cytoplasm. Yet, it remains unclear how spatially restricted biochemical signals are integrated with physical properties of the embryo to generate collective dynamics. Here, we show that synchronization of the cell cycle in Drosophila embryos requires accurate nuclear positioning, which is regulated by the cell-cycle oscillator through cortical contractility and cytoplasmic flows. We demonstrate that biochemical oscillations are initiated by local Cdk1 inactivation and spread through the activity of phosphatase PP1 to generate cortical myosin II gradients. These gradients cause cortical and cytoplasmic flows that control proper nuclear positioning. Perturbations of PP1 activity and optogenetic manipulations of cortical actomyosin disrupt nuclear spreading, resulting in loss of cell-cycle synchrony. We conclude that mitotic synchrony is established by a self-organized mechanism that integrates the cell-cycle oscillator and embryo mechanics.

Geometric Effect for Biological Reactors and Biological Fluids

As expressed "God made the bulk; the surface was invented by the devil" by W. Pauli, the surface has remarkable properties because broken symmetry in surface alters the material properties. In biological systems, the smallest functional and structural unit, which has a functional bulk space enclosed by a thin interface, is a cell. Cells contain inner cytosolic soup in which genetic information stored in DNA can be expressed through transcription (TX) and translation (TL). The exploration of cell-sized confinement has been recently investigated by using micron-scale droplets and microfluidic devices. In the first part of this review article, we describe recent developments of cell-free bioreactors where bacterial TX-TL machinery and DNA are encapsulated in these cell-sized compartments. Since synthetic biology and microfluidics meet toward the bottom-up assembly of cell-free bioreactors, the interplay between cellular geometry and TX-TL advances better control of biological structure and dynamics in vitro system. Furthermore, biological systems that show self-organization in confined space are not limited to a single cell, but are also involved in the collective behavior of motile cells, named active matter. In the second part, we describe recent studies where collectively ordered patterns of active matter, from bacterial suspensions to active cytoskeleton, are self-organized. Since geometry and topology are vital concepts to understand the ordered phase of active matter, a microfluidic device with designed compartments allows one to explore geometric principles behind self-organization across the molecular scale to cellular scale. Finally, we discuss the future perspectives of a microfluidic approach to explore the further understanding of biological systems from geometric and topological aspects.

Operating in a Reverberating Regime Enables Rapid Tuning of Network States to Task Requirements

Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a “reverberating regime” because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call “dynamic adaptive computation,” presents a central organization principle of cortical networks and discuss first experimental evidence.

Clathrin Assembly Defines the Onset and Geometry of Cortical Patterning

Assembly of the endocytic machinery is a constitutively active process that is important for the organization of the plasma membrane, signal transduction, and membrane trafficking. Existing research has focused on the stochastic nature of endocytosis. Here, we report the emergence of the collective dynamics of endocytic proteins as periodic traveling waves on the cell surface. Coordinated clathrin assembly provides the earliest spatial cue for cortical waves and sets the direction of propagation. Surprisingly, the onset of clathrin waves, but not individual endocytic events, requires feedback from downstream factors, including FBP17, Cdc42, and N-WASP. In addition to the localized endocytic assembly at the plasma membrane, intracellular clathrin and phosphatidylinositol-3,4-bisphosphate predict the excitability of the plasma membrane and modulate the geometry of traveling waves. Collectively, our data demonstrate the multiplicity of clathrin functions in cortical pattern formation and provide important insights regarding the nucleation and propagation of single-cell patterns.

Intermittent turbulence in flowing bacterial suspensions

Dense suspensions of motile bacteria, possibly including the human gut microbiome, exhibit collective dynamics akin to those observed in classic, high Reynolds number turbulence with important implications for chemical and biological transport, yet this analogy has remained primarily qualitative. Here, we present experiments in which a dense suspension of Bacillus subtilis bacteria was flowed through microchannels and the velocity statistics of the flowing suspension were quantified using a recently developed velocimetry technique coupled with vortex identification methods. Observations revealed a robust intermittency phenomenon, whereby the average velocity profile of the suspension fluctuated between a plug-like flow and a parabolic flow profile. This intermittency is a hallmark of the onset of classic turbulence and Lagrangian tracking revealed that it here originates from the presence of transient vortices in the active, collective motion of the bacteria locally reinforcing the externally imposed flow. These results link together two entirely different manifestations of turbulence and show the potential of the microfluidic approach to mimic the environment characteristic of certain niches of the human microbiome.

Simple phalanx pattern leads to energy saving in cohesive fish schooling

The question of how individuals in a population organize when living in groups arises for systems as different as a swarm of microorganisms or a flock of seagulls. The different patterns for moving collectively involve a wide spectrum of reasons, such as evading predators or optimizing food prospection. Also, the schooling pattern has often been associated with an advantage in terms of energy consumption. In this study, we use a popular aquarium fish, the red nose tetra fish, Hemigrammus bleheri, which is known to swim in highly cohesive groups, to analyze the schooling dynamics. In our experiments, fish swim in a shallow-water tunnel with controlled velocity, and stereoscopic video recordings are used to track the 3D positions of each individual in a school, as well as their tail-beating kinematics. Challenging the widespread idea of fish favoring a diamond pattern to swim more efficiently [Weihs D (1973) Nature 241:290-291], we observe that when fish are forced to swim fast-well above their free-swimming typical velocity, and hence in a situation where efficient swimming would be favored-the most frequent configuration is the "phalanx" or "soldier" formation, with all individuals swimming side by side. We explain this observation by considering the advantages of tail-beating synchronization between neighbors, which we have also characterized. Most importantly, we show that schooling is advantageous as compared with swimming alone from an energy-efficiency perspective.

Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation.NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs.

Understanding the group dynamics and success of teams

Complex problems often require coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large-scale, quantitative data. We analyse activity traces and success levels for approximately 150 000 self-organized, online team projects. While larger teams tend to be more successful, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or 'leads' of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavours.

Biophysical basis for convergent evolution of two veil-forming microbes

Microbes living in stagnant water typically rely on chemical diffusion to draw nutrients from their environment. The sulfur-oxidizing bacterium Thiovulum majus and the ciliate Uronemella have independently evolved the ability to form a 'veil', a centimetre-scale mucous sheet on which cells organize to produce a macroscopic flow. This flow pulls nutrients through the community an order of magnitude faster than diffusion. To understand how natural selection led these microbes to evolve this collective behaviour, we connect the physical limitations acting on individual cells to the cell traits. We show how diffusion limitation and viscous dissipation have led individual T. majus and Uronemella cells to display two similar characteristics. Both of these cells exert a force of approximately 40 pN on the water and attach to boundaries by means of a mucous stalk. We show how the diffusion coefficient of oxygen in water and the viscosity of water define the force the cells must exert. We then show how the hydrodynamics of filter-feeding orient a microbe normal to the surface to which it attaches. Finally, we combine these results with new observations of veil formation and a review of veil dynamics to compare the collective dynamics of these microbes. We conclude that this convergent evolution is a reflection of similar physical limitations imposed by diffusion and viscosity acting on individual cells.

Optogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortex

Transient gamma-band (40-80 Hz) spatiotemporal patterns are hypothesized to play important roles in cortical function. Here we report the direct observation of gamma oscillations as spatiotemporal waves induced by targeted optogenetic stimulation, recorded by intracortical multichannel extracellular techniques in macaque monkeys during their awake resting states. Microelectrode arrays integrating an optical fiber at their center were chronically implanted in primary motor (M1) and ventral premotor (PMv) cortices of two subjects. Targeted brain tissue was transduced with the red-shifted opsin C1V1(T/T). Constant (1-s square pulses) and ramp stimulation induced narrowband gamma oscillations during awake resting states. Recordings across 95 microelectrodes (4 × 4-mm array) enabled us to track the transient gamma spatiotemporal patterns manifested, e.g., as concentric expanding and spiral waves. Gamma oscillations were induced well beyond the light stimulation volume, via network interactions at distal electrode sites, depending on optical power. Despite stimulation-related modulation in spiking rates, neuronal spiking remained highly asynchronous during induced gamma oscillations. In one subject we examined stimulation effects during preparation and execution of a motor task and observed that movement execution largely attenuated optically induced gamma oscillations. Our findings demonstrate that, beyond previously reported induced gamma activity under periodic drive, a prolonged constant stimulus above a certain threshold may carry primate motor cortex network dynamics into gamma oscillations, likely via a Hopf bifurcation. More broadly, the experimental capability in combining microelectrode array recordings and optogenetic stimulation provides an important approach for probing spatiotemporal dynamics in primate cortical networks during various physiological and behavioral conditions.

Group personality during collective decision-making: a multi-level approach

Collective decision-making processes emerge from social feedback networks within a group. Many studies on collective behaviour underestimate the role of individual personality and, as a result, personality is rarely analysed in the context of collective dynamics. Here, we show evidence of sheltering behaviour personality in a gregarious insect (Periplaneta americana), which is characterized by a collective personality at the group level. We also highlight that the individuals within groups exhibited consistent personality traits in their probability of sheltering and total time sheltered during the three trials over one week. Moreover, the group personality, which arises from the synergy between the distribution of behaviour profiles in the group and social amplifications, affected the sheltering dynamics. However, owing to its robustness, personality did not affect the group probability of reaching a consensus. Finally, to prove social interactions, we developed a new statistical method that will be helpful for future research on personality traits and group behaviour. This approach will help to identify the circumstances under which particular group compositions may improve the fitness of individuals in gregarious species.

Highly cooperative stress relaxation in two-dimensional soft colloidal crystals

Stress relaxation in crystalline solids is mediated by the formation and diffusion of defects. Although it is well established how externally generated stresses relax, through the proliferation and motion of dislocations in the lattice, it remains relatively unknown how crystals cope with internal stresses. We investigate, both experimentally and in simulations, how highly localized stresses relax in 2D soft colloidal crystals. When a single particle is actively excited, by means of optical tweezing, a rich variety of highly collective stress relaxation mechanisms results. These relaxation processes manifest in the form of open strings of cooperatively moving particles through the motion of dissociated vacancy-interstitial pairs, and closed loops of mobile particles, which either result from cooperative rotations in transiently generated circular grain boundaries or through the closure of an open string by annihilation of a vacancy-interstitial pair. Surprisingly, we find that the same collective events occur in crystals that are excited by thermal fluctuations alone; a large thermal agitation inside the crystal lattice can trigger the irreversible displacements of hundreds of particles. Our results illustrate how local stresses can induce large-scale cooperative dynamics in 2D soft colloidal crystals and shed light on the stabilization mechanisms in ultrasoft crystals.

Neuronal ensemble synchrony during human focal seizures

Seizures are classically characterized as the expression of hypersynchronous neural activity, yet the true degree of synchrony in neuronal spiking (action potentials) during human seizures remains a fundamental question. We quantified the temporal precision of spike synchrony in ensembles of neocortical neurons during seizures in people with pharmacologically intractable epilepsy. Two seizure types were analyzed: those characterized by sustained gamma (∼40-60 Hz) local field potential (LFP) oscillations or by spike-wave complexes (SWCs; ∼3 Hz). Fine (<10 ms) temporal synchrony was rarely present during gamma-band seizures, where neuronal spiking remained highly irregular and asynchronous. In SWC seizures, phase locking of neuronal spiking to the SWC spike phase induced synchrony at a coarse 50-100 ms level. In addition, transient fine synchrony occurred primarily during the initial ∼20 ms period of the SWC spike phase and varied across subjects and seizures. Sporadic coherence events between neuronal population spike counts and LFPs were observed during SWC seizures in high (∼80 Hz) gamma-band and during high-frequency oscillations (∼130 Hz). Maximum entropy models of the joint neuronal spiking probability, constrained only on single neurons' nonstationary coarse spiking rates and local network activation, explained most of the fine synchrony in both seizure types. Our findings indicate that fine neuronal ensemble synchrony occurs mostly during SWC, not gamma-band, seizures, and primarily during the initial phase of SWC spikes. Furthermore, these fine synchrony events result mostly from transient increases in overall neuronal network spiking rates, rather than changes in precise spiking correlations between specific pairs of neurons.

Terahertz Dynamics in Human Cells and Their Chromatin

The terahertz dynamics of human cells of the U937 line and their chromatin has been investigated by high-resolution inelastic X-ray scattering. To highlight its dynamical features in situ, nuclear DNA has been stained by uranyl-acetate salt. The general behavior of the collective dynamics of the whole cell is quite similar to that of bulk water, with a nearly wavevector-independent branch located at about 5 meV and a propagating mode with a linear trend corresponding to a speed of sound of 2900 ± 100 m/s. We provide the first experimental evidence for the existence of two branches also in the dispersion curves of chromatin. The high-energy mode displays an acoustic-like behavior with a sound velocity similar to unstained cells, but in this case the branch likely originates from the superposition of intramolecular DNA optic modes. A low-energy optic-like branch, distinctive of the chromatin moiety, is found at about 2.5 meV.

Inelastic Neutron Scattering Investigation in Glassy SiSe2: Complex Dynamics at the Atomic Scale

A detailed investigation of the THz dynamics in glassy SiSe2 by means of neutron inelastic scattering is presented. To carefully map the translational dynamics and the region of the boson peak, we carried out two different experiments with sharp and broad resolutions coupled with a narrow and a wide kinematic range, respectively. Data show a complex pattern of excitations made up of three components. The most intense one is the prolongation of the longitudinal acoustic mode while two other modes appear in the boson peak region below 3 meV. We propose an interaction model that allows for a consistent identification of the nature of these modes.

Swarming and pattern formation due to selective attraction and repulsion

We discuss the collective dynamics of self-propelled particles with selective attraction and repulsion interactions. Each particle, or individual, may respond differently to its neighbours depending on the sign of their relative velocity. Thus, it is able to distinguish approaching (coming closer) and retreating (moving away) individuals. This differentiation of the social response is motivated by the response to looming visual stimuli and may be seen as a generalization of the previously proposed escape and pursuit interactions motivated by empirical evidence for cannibalism as a driving force of collective migration in locusts and Mormon crickets. The model can account for different types of behaviour such as pure attraction, pure repulsion or escape and pursuit, depending on the values (signs) of the different response strengths. It provides, in the light of recent experimental results, an interesting alternative to previously proposed models of collective motion with an explicit velocity-alignment interaction. We discuss the derivation of a coarse-grained description of the system dynamics, which allows us to derive analytically the necessary condition for emergence of collective motion. Furthermore, we analyse systematically the onset of collective motion and clustering in numerical simulations of the model for varying interaction strengths. We show that collective motion arises only in a subregion of the parameter space, which is consistent with the analytical prediction and corresponds to an effective escape and/or pursuit response.

From isolated to networked: a paradigmatic shift in mitochondrial physiology

A new paradigm of mitochondrial function in networks is emerging which includes, without undermining, the glorious and still useful paradigm of the isolated mitochondrion. The mitochondrial network paradigm introduces new concepts, tools, and analytical techniques. Among them is that mitochondrial function in networks exhibits interdependence and multiplicative effects based on synchronization mechanisms, which involve communication between mitochondrial neighbors. The collective dynamics of these networks become advantageous for coordinating function spanning from the cell, to the tissue, and the organ. However, under severely stressful conditions the network behavior of mitochondria may become life threatening.

Exploring Collective Dynamics in Communication Networks

A communication network, such as the Internet, comprises a complex system where cooperative phenomena may emerge from interactions among various traffic flows generated and forwarded by individual nodes. To identify and understand such phenomena, we model a network as a two-dimensional cellular automaton. We suspect such models can promote better understanding of the spatial-temporal evolution of network congestion, and other emergent phenomena in communication networks. To search the behavior space of the model, we study dynamic patterns arising from interactions among traffic flows routed across shared network nodes, as we employ various configurations of parameters and two different congestion-control algorithms. In this paper, we characterize correlation in congestion behavior within the model at different system sizes and time granularities. As expected, we find that long-range dependence (LRD) appears at some time granularities, and that for a given network size LRD decays as time granularity increases. As network size increases, we find that long-range dependence exists at larger time scales. To distinguish effects due to network size from effects due to collective phenomena, we compare congestion behavior within networks of selected sizes to congestion behavior within comparably sized sub-areas in a larger network. We find stronger long-range dependence for sub-areas within the larger network. This suggests the importance of modeling networks of sufficiently large size when studying the effects of collective dynamics.