PLoS Computational Biology最新文献

Neural circuits often alternate between tonic and burst firing, two distinct activity regimes that reflect changes in excitability and neuromodulatory state. While tonic firing produces asynchronous spiking driven by diverse external inputs, collective burst firing consists of rapid clusters of spikes followed by a period of silence, happening synchronously within the network. Synaptic plasticity has typically been studied only in either one of these regimes, leaving unclear how their distinct plasticity dynamics can be combined when circuits alternate between regimes. Here, we use a conductance-based network model endowed with calcium-based or spike-timing-based plasticity rules to examine how synaptic weights evolve across tonic and burst firing regimes. During tonic firing, synaptic weights are driven by the statistics of external inputs, producing a broad distribution across the network. In contrast, during collective burst firing, weights converge to a narrow region in weight space: a burst-induced attractor. We derive the location of this attractor analytically in terms of plasticity parameters and activity statistics, and confirm its emergence across diverse plasticity rules. The attractor reflects the synchronization of plasticity-driving signals during bursts, which homogenizes synaptic dynamics and forces convergence toward shared fixed points. We further show that neuromodulation and synaptic tagging can shift or split the burst-induced attractor, stabilizing selected synapses while weakening others. Together, these results identify burst-induced attractors as a robust emergent property of collective bursting. Alternation between tonic and burst firing provides a biologically plausible context in which heterogeneous, input-driven synaptic configurations formed during tonic activity can be selectively consolidated or down-selected by the burst-induced attractor during subsequent bursts. By showing how they can be analytically predicted and experimentally modulated, our work provides a general computational framework linking firing state transitions, synaptic plasticity, and memory organization.

Integrated Information Theory is a theoretical framework proposing that consciousness is a fundamental property of systems capable of integrating information. To bridge the gap between the theoretical concept and the practical use in actual neurobiological systems, we have applied the Integrated Information Theory approach to a simulated network of integrate and fire neurons (IAF). The primary contribution of this study is several empirical findings. Our analysis shows that such a network can possess a non-zero Φ value under certain conditions and parameter settings. Additionally, our research indicates that the complexity of the network's dynamics doesn't necessarily correlate with its Φ value. On the other hand, the quantity of integrated information within the network appears to grow with the IAF neurons' time constant, which reflects their integrative capacity. Furthermore, our examination of the integrate and fire network with internal random fluctuations demonstrates that the integrated information measure, as defined in IIT version 3.0, is not resilient to noise.

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. Although these phenomena have been extensively investigated experimentally and theoretically, the principles governing how E and I inputs are integrated remain unclear. Here, probabilistic rules are derived to describe how neurons in feedforward inhibitory circuits combine these inputs to generate stimulus-evoked responses. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models, and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns.

Although the cortico-basal ganglia-thalamic (CBGT) network is identified as a central circuit for decision-making, the dynamic interplay of multiple control pathways within this network in shaping decision trajectories remains poorly understood. Here we develop and apply a novel computational framework-CLAW (Circuit Logic Assessed via Walks)-for tracing the instantaneous flow of neural activity as it progresses through CBGT networks engaged in a virtual decision-making task. Our CLAW analysis reveals that the complex dynamics of network activity is functionally dissectible into two critical phases: deliberation and commitment. These two phases are governed by distinct contributions of underlying CBGT pathways, with indirect and pallidostriatal pathways influencing deliberation, while the direct pathway drives action commitment. We translate CBGT dynamics into the evolution of decision-related policies, based on three previously identified control ensembles (responsiveness, pliancy, and choice) that encapsulate the relationship between CBGT activity and the evidence accumulation process. Our results demonstrate two contrasting strategies for decision-making. Fast decisions, with direct pathway dominance, feature an early response in both boundary height and drift rate, leading to a rapid collapse of decision boundaries and a clear directional bias. In contrast, slow decisions, driven by indirect and pallidostriatal pathway dominance, involve delayed changes in both decision policy parameters, allowing for an extended period of deliberation before commitment to an action. These analyses provide important insights into how the CBGT circuitry can be tuned to adopt various decision strategies and how the decision-making process unfolds within each regime.

Reinforcement learning models can be combined with sequential sampling models to fit choice-RT data. The combined models, known as RL-SSMs, explain a wide range of choice-RT patterns in repeated decision tasks. The present study shows how constraining an RL-SSM with eye gaze data can further enhance its predictive ability. Our model allows learned option values and relative gaze to jointly influence the accumulation of evidence prior to choice. We evaluate the model on data from two eye-tracking experiments (total N = 133) and test several variants of the model that assume different mechanisms for integrating values and gaze at the decision stage. Further, we show that it captures a variety of empirical effects, including gaze biases on choice and response time, as well as individual differences in absolute versus relative valuation. The model can be used to understand how learned option values interact with visual attention to influence choice, joining together two major (but mostly separate) modeling traditions.

While the global health burden of COVID-19 continues, multifaceted epidemiological surveillance is required to monitor the epidemic's dynamics and its population-wide risk. By collecting information that is used in conventional vaccine effectiveness studies through questionnaire surveys, we proposed a simple framework using a population-wide snapshot questionnaire survey to estimate the incidence and protective effect of immunity by natural infection or vaccination against the SARS-CoV-2 JN.1 variant. Our results revealed that in Japan in February 2024, the personal risk of diagnosed infection was substantially higher in younger adults and risk was heterogenous across prefectures. Diabetes mellitus (relative hazard ratio 1.8; 95% credible interval [CrI] 1.1, 2.9), neoplastic disorders (5.2; 95% CrI 3.1, 8.6), immunological suppression (2.6; 95% CrI 1.3, 4.6), respiratory diseases (2.2; 95% CrI 1.4, 3.3), and cardiovascular disease (2.3; 95% CrI 1.3, 3.9) were risk factors for diagnosed infection. The highest peak protection after infection was after exposure to pre-XBB.1.5 Omicron variants (52.0%; 95% CrI 33.2, 68.7), whereas the XBB.1.5 monovalent vaccine provided the highest protection (45.1%; 95% CrI 37.8, 52.7) among three vaccine types. Notably, the peak protection of the bivalent Wuhan + Omicron BA.1/5 vaccine was substantially lower than other vaccines (28.7; 95% CrI 17.3, 40.6). By statistically matching the respondent cohort to the 2020 population census, we revealed that the national COVID-19 incidence rate in February 2024 by age group was highest (4.73%; 95% CrI 4.17, 5.38) and lowest (1.19%; 95% CrI 0.94, 1.47) among those aged 20-29 years and 60-69 years, respectively. The force of infection measured by diagnosed infection was high and more heterogeneous in younger groups, whereas younger populations were more concentrated than older populations in low-protection regions. Our framework revealed biological and epidemiological insights into protection and risk of diagnosed infection from past immunizing events and personal attributes during the JN.1-dominant period. Moreover, we proposed a framework for the rapid evaluation of epidemiological dynamics whose application is not limited to COVID-19.

Plastic stabilizers (PSs) are chemical additives that are widely used to inhibit the degradation of plastics. However, their safety concerns and potential carcinogenic risks remain unclear. This study employed network toxicology strategies to elucidate the potential toxic effects and underlying molecular mechanisms of representative PSs, including 2,6-di-tert-butylphenol (2,6-DTB), tert-butylhydroquinone (TBHQ), and 2-(2H-benzotriazol-2-yl)-4,6-di-tert-pentylphenol (UV-328) in breast cancer (BC). Herein, we identified 69 potential genes related to PSs exposure and BC, and optimized five core targets: GSK3B, MAPK14, PARP1, PIM1, and TRDMT1, through subsequent LASSO and SVM algorithms. Based on these core genes, we constructed risk score and nomogram models, both of which revealed that high expression of these five core genes predicts poor prognosis in BC patients. Additionally, molecular docking and dynamic simulations indicated high-affinity interactions between PSs and these core targets (binding energies < -5 kcal/mol). Further correlation analysis with prediction analysis of microarray 50 (PAM50) revealed increased expression of all core genes in the basal-like subtype, especially PIM1 and TRDMT1, which also exhibited the highest risk scores. In vitro, PSs transcriptionally upregulated MAPK14, PIM1, and TRDMT1, with STAT3 mediating their transcription. Importantly, cell counting kit-8 and wound healing assays demonstrated that PSs promote BC cell proliferation and migration. Our research re-evaluates the carcinogenic risks of plastic stabilizers and suggests that PSs may enhance breast cancer progression via targets such as MAPK14, PIM1, and TRDMT1. This study introduces a new approach for evaluating the safety of plastic additives and offers novel insights into the toxicological effects of PSs.

Passive acoustic monitoring (PAM) is a powerful tool for studying marine biodiversity, but large-scale analysis of underwater recordings is constrained by noise, overlapping signals, and limited labeled data. Here, we present a scalable, unsupervised contrastive learning framework for marine soundscapes. Using a large PAM dataset spanning multiple biogeographies, we show that the proposed approach organizes recordings into clusters with well-defined internal structure, as assessed using intrinsic clustering metrics and within-cluster similarity. The resulting clusters reveal recurring acoustic patterns that correspond to broad sound-source categories, including biological sounds such as fish calls and choruses, and anthropogenic sounds such as vessel noise, without explicitly enforcing these distinctions during training. Compared with established approaches, including cepstral features, variational autoencoders, and supervised pipelines, the proposed framework produces embeddings that support more compact and stable unsupervised clustering while preserving fine-scale acoustic variation beyond predefined species labels. By learning a shared representation across recordings from multiple sites and years, we examine the reproducibility of acoustic patterns across locations and identify both site-shared and site-specific sound signatures. Although the method is not designed to recover coarse species labels, it enables label-efficient analysis by reducing reliance on manual annotation and supporting exploratory characterization of complex marine soundscapes. Together, these results highlight multi-positive contrastive learning with a teacher network and acoustically informed augmentations as an effective strategy for scalable, discovery-driven analysis of passive acoustic monitoring data.

The identification of essential genes in Transposon Directed Insertion Site Sequencing (TraDIS) data relies on the assumption that transposon insertions occur randomly in non-essential regions, leaving essential genes largely insertion-free. While intragenic insertion-free sequences have been considered as a reliable indicator for gene essentiality, so far, no exact probability distribution for these sequences has been proposed. Further, many methods require setting thresholds or parameter values a priori without providing any statistical basis, limiting the comparability of results. Here, we introduce Consecutive Non-Insertion Sites (ConNIS), a novel method for gene essentiality determination. ConNIS provides an analytic solution for the probability of observing insertion-free sequences within genes of given length and considers variation in insertion density across the genome. Based on an extensive simulation study and different real-world scenarios, ConNIS was found to be superior to prevalent state-of-the-art methods, particularly when libraries had only a low or medium insertion density. In addition, our results showed that the precision of existing methods can be improved by incorporating a simple weighting factor for the genome-wide insertion density. To set methodically embedded parameter and threshold values of TraDIS methods a subsample-based instability criterion was developed. Application of this criterion in real and synthetic data settings demonstrated its effectiveness in selecting well-suited parameter/threshold values across methods. An R package and an interactive web application are provided to facilitate application and reproducibility.

The Golgi apparatus has an intricate spatial structure characterized by flattened membrane-bound compartments, known as cisternae. Cisternae house integral membrane enzymes that catalyse glycosylation, the addition of polymeric sugars to protein cargo, which is important for the trafficking and function of the products. The unusual and specific shape of Golgi cisternae is highly conserved across eukaryotic cells, suggesting significant influence in the correct functioning of the Golgi. Motivated by experimental evidence that disruption to Golgi morphology can lead to observable changes in secreted cargo mass distribution, we develop and analyse a mathematical model of polymerisation in a cisterna that combines chemical kinetics, spatial diffusion and adsorption and desorption between lumen and membrane. Exploiting the slender geometry, we derive a non-local non-linear advection-diffusion equation that predicts secreted cargo mass distribution as a function of cisternal shape. The model predicts a maximum cisternal thickness for which successful glycosylation is possible, demonstrates the existence of an optimal thickness for most efficient glycosylation, and suggests how kinetic and geometric factors may combine to promote or disrupt polymer production.