arXiv subjects

Christopher W. Lynn

Publications and source records attributed to Christopher W. Lynn.

35 records · Page 2Linked to original sources

arXiv2022

Emergent scale-free networks

Many complex systems--from social and communication networks to biological networks and the Internet--are thought to exhibit scale-free structure. However, prevailing explanations rely on the constant addition of new nodes, an assumption that fails dramatically in some real-world settings. Here, we propose a model in which nodes are allowed to die, and their connections rearrange under a mixture of preferential and random attachment. With these simple dynamics, we show that networks self-organize towards scale-free structure, with a power-law exponent $γ= 1 + \frac{1}{p}$ that depends only on the proportion $p$ of preferential (rather than random) attachment. Applying our model to several real networks, we infer $p$ directly from data, and predict the relationship between network size and degree heterogeneity. Together, these results establish that realistic scale-free structure can emerge naturally in networks of constant size and density, with broad implications for the structure and function of complex systems.

nlin.AO↗

arXiv2022

Emergence of local irreversibility in complex interacting systems

Living systems are fundamentally irreversible, breaking detailed balance and establishing an arrow of time. But how does the evident arrow of time for a whole system arise from the interactions among its multiple elements? We show that the local evidence for the arrow of time, which is the entropy production for thermodynamic systems, can be decomposed. First, it can be split into two components: an independent term reflecting the dynamics of individual elements and an interaction term driven by the dependencies among elements. Adapting tools from non--equilibrium physics, we further decompose the interaction term into contributions from pairs of elements, triplets, and higher--order terms. We illustrate our methods on models of cellular sensing and logical computations, as well as on patterns of neural activity in the retina as it responds to visual inputs. We find that neural activity can define the arrow of time even when the visual inputs do not, and that the dominant contribution to this breaking of detailed balance comes from interactions among pairs of neurons.

cond-mat.stat-mech↗

arXiv2022

Decomposing the local arrow of time in interacting systems

We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non--negative terms contributed by correlations among pairs, triplets, and higher--order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons.

cond-mat.stat-mech↗

arXiv2022

Curiosity as filling, compressing, and reconfiguring knowledge networks

Due to the significant role that curiosity plays in our lives, several theoretical constructs, such as the information gap theory and compression progress theory, have sought to explain how we engage in its practice. According to the former, curiosity is the drive to acquire information that is missing from our understanding of the world. According to the latter, curiosity is the drive to construct an increasingly parsimonious mental model of the world. To complement the densification processes inherent to these theories, we propose the conformational change theory, wherein we posit that curiosity results in mental models with marked conceptual flexibility. We formalize curiosity as the process of building a growing knowledge network to quantitatively investigate information gap theory, compression progress theory, and the conformational change theory of curiosity. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We leverage data acquired from the online encyclopedia Wikipedia to determine the degree to which each theory explains the growth of knowledge networks built by individuals and by collectives. Our findings lend support to a pluralistic view of curiosity, wherein intrinsically motivated information acquisition fills knowledge gaps and simultaneously leads to increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each theoretical account may be explanatory, thereby clarifying their complementary and distinct explanations of curiosity. Our findings offer a novel network theoretical perspective on intrinsically motivated information acquisition that may harmonize with or compel an expansion of the traditional taxonomy of curiosity.

q-bio.NC↗

arXiv2021

Citation inequity and gendered citation practices in contemporary physics

The historical and contemporary under-attribution of women's contributions to scientific scholarship is well-known and well-studied, with effects that are felt today in myriad ways by women scientists. One measure of this under-attribution is the so-called citation gap between men and women: the under-citation of papers authored by women relative to expected rates coupled with a corresponding over-citation of papers authored by men relative to expected rates. We explore the citation gap in contemporary physics, analyzing over one million articles published over the last 25 years in 35 physics journals that span a wide range of subfields. Using a model that predicts papers' expected citation rates according to a set of characteristics separate from author gender, we find a global bias wherein papers authored by women are significantly under-cited, and papers authored by men are significantly over-cited. Moreover, we find that citation behavior varies along several dimensions, such that imbalances differ according to who is citing, where they are citing, and how they are citing. Specifically, citation imbalance in favor of man-authored papers is highest for papers authored by men, papers published in general physics journals, and papers likely to be less familiar to citing authors. Our results suggest that, although deciding which papers to cite is an individual choice, the cumulative effects of these choices needlessly harm a subset of scholars. We discuss several strategies for the mitigation of these effects, including conscious behavioral changes at the individual, journal, and community levels.

physics.soc-ph↗

arXiv2021

Optimizing the Human Learnability of Abstract Network Representations

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To answer these questions, we study the optimization of network learnability. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

physics.soc-ph↗

arXiv2021

Efficient Coding in the Economics of Human Brain Connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond a conventional metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

q-bio.NC↗

arXiv2021

Broken detailed balance and entropy production in the human brain

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales, and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition, and provide a general tool for quantifying entropy production in macroscopic systems.

physics.bio-ph↗

arXiv2021

Compressibility of complex networks

Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding, or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization -- which is characterized by modular structure and heterogeneous degrees -- facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network's structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

physics.soc-ph↗

arXiv2020

How We Learn About our Networked World

When presented with information of any type, from music to language to mathematics, the human mind subconsciously arranges it into a network. A network puts pieces of information like musical notes, syllables or mathematical concepts into context by linking them together. These networks help our minds organize information and anticipate what is coming. Here we present two questions about network building. 1) Can humans more easily learn some types of networks than others? 2) Do humans find some links between ideas more surprising than others? The answer to both questions is "Yes," and we explain why. The findings provide much-needed insight into the ways that humans learn about the networked world around them. Moreover, the study paves the way for future efforts seeking to optimize how information is presented to accelerate human learning.

q-bio.NC↗

arXiv2020

Abstract representations of events arise from mental errors in learning and memory

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources.

q-bio.NC↗

arXiv2020

Human information processing in complex networks

Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many communication networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.

physics.soc-ph↗

arXiv2020

How humans learn and represent networks

Humans communicate, receive, and store information using sequences of items -- from words in a sentence or notes in music to abstract concepts in lectures and books. The networks formed by these items (nodes) and the sequential transitions between them (edges) encode important structural features of human communication and knowledge. But how do humans learn the networks of probabilistic transitions that underlie sequences of items? Moreover, what do people's internal maps of these networks look like? Here, we introduce graph learning, a growing and interdisciplinary field focused on studying how humans learn and represent networks in the world around them. We begin by describing established results from statistical learning showing that humans are adept at detecting differences in the transition probabilities between items in a sequence. We next present recent experiments that directly control for differences in transition probabilities, demonstrating that human behavior also depends critically on the abstract network structure of transitions. Finally, we present computational models that researchers have proposed to explain the effects of network structure on human behavior and cognition. Throughout, we highlight a number of exciting open questions in the study of graph learning that will require creative insights from cognitive scientists and network scientists alike.

physics.soc-ph↗

arXiv2020

Relations between large scale brain connectivity and effects of regional stimulation depend on collective dynamical state

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display rhythmic dynamics that support interareal communication. Understanding how stimulation of a particular brain area impacts such concerted activity is important for gaining basic insights into brain function and for developing neuromodulation as a therapeutic tool. However, it remains difficult to predict the downstream effects of focal stimulation. Specifically, little is known about how the collective oscillatory regime of network activity may affect the outcomes of regional perturbations on cooperative dynamics. Here, we combine connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control the collective dynamics of the network, we identify distinct states of simulated brain activity, and investigate how the distributed effects of stimulation manifest in different states. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, nearby regions develop phase locked activity in the excited frequency band. Importantly, we find that focal stimulation also causes more distributed modifications to network coherence at regions' baseline oscillation frequencies, and that these effects are better predicted by functional rather than structural connectivity. In contrast, when the network operates in a regime of stronger endogenous oscillations, stimulation causes only slight shifts in power and frequency, and network averaged changes in coherence are more homogenous across the choice of the stimulated area. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and highlights that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network wide responses to local perturbations.

q-bio.NC↗

arXiv2019

The physics of brain network structure, function, and control

The brain is a complex organ characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviors. New noninvasive imaging techniques now allow these patterns to be carefully and comprehensively mapped in individual humans and animals. Yet, it remains a fundamental challenge to understand how the brain's structural wiring supports cognitive processes, with major implications for the personalized treatment of mental health disorders. Here, we review recent efforts to meet this challenge that draw on intuitions, models, and theories from physics, spanning the domains of statistical mechanics, information theory, and dynamical systems and control. We begin by considering the organizing principles of brain network architecture instantiated in structural wiring under constraints of symmetry, spatial embedding, and energy minimization. We next consider models of brain network function that stipulate how neural activity propagates along these structural connections, producing the long-range interactions and collective dynamics that support a rich repertoire of system functions. Finally, we consider perturbative experiments and models for brain network control, which leverage the physics of signal transmission along structural wires to infer intrinsic control processes that support goal-directed behavior and to inform stimulation-based therapies for neurological disease and psychiatric disorders. Throughout, we highlight several open questions in the physics of brain network structure, function, and control that will require creative efforts from physicists willing to brave the complexities of living matter.

q-bio.NC↗

arXiv2019

Surges of collective human activity emerge from simple pairwise correlations

Human populations exhibit complex behaviors---characterized by long-range correlations and surges in activity---across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from, or if there even exists a set of unifying principles. Indeed, existing explanations typically rely on context-specific mechanisms, such as traffic jams driven by work schedules or spikes in online traffic induced by significant events. However, analogies with statistical mechanics suggest a more general mechanism: that collective patterns can emerge organically from fine-scale interactions within a population. Here, across four different modes of human activity, we show that the simplest correlations in a population---those between pairs of individuals---can yield accurate quantitative predictions for the large-scale behavior of the entire population. To quantify the minimal consequences of pairwise correlations, we employ the principle of maximum entropy, making our description equivalent to an Ising model whose interactions and external fields are notably calculated from past observations of population activity. In addition to providing accurate quantitative predictions, we show that the topology of learned Ising interactions resembles the network of inter-human communication within a population. Together, these results demonstrate that fine-scale correlations can be used to predict large-scale social behaviors, a perspective that has critical implications for modeling and resource allocation in human populations.

physics.soc-ph↗

arXiv2018

Maximizing Activity in Ising Networks via the TAP Approximation

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the naïve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system.

physics.soc-ph↗