79 papers
This paper introduces a biologically plausible associative memory network that overcomes the linear capacity limitation of previous winner-take-all models by employing a threshold nonlinearity to enable distributed hidden representations, thereby achieving exponential memory storage capacity relative to the number of hidden units.
This paper demonstrates that incorporating domain-aware priors, specifically gradient saliency-guided feature selection with biological constraints, significantly stabilizes vertical federated learning for coral multi-omics classification under extreme data scarcity (P >> N), enabling the REEF framework to substantially outperform generic and state-of-the-art baselines while reducing variance and ensuring interpretability.
This review article surveys phenomenological and microscopic models used to describe the complex, non-Arrhenius temperature responses of biological systems across various scales, defining key operational metrics like optimal temperatures and thermal limits while setting the stage for a subsequent discussion on how system-level curves emerge from interacting reactions.
This study demonstrates that in older adults with subjective cognitive decline, the brain's ability to track higher-level linguistic features during prosodically flat speech is significantly weakened, suggesting this specific neural deficit could serve as an early biomarker for cognitive decline.
This paper presents an extended mechanistic model of CD4+/CD8+ CAR-T cell dynamics regulated by tumor antigen burden, demonstrating how combining sensitivity analysis with machine learning can elucidate treatment drivers and partially recover predictive accuracy from noisy patient data despite parameter uncertainty.
This paper demonstrates that introducing a "dreaming" phase, where random patterns are unlearned, significantly enhances the memorization capacity of a clipped Hopfield model that avoids catastrophic forgetting by incorporating biologically plausible bounded synaptic strengths.
The paper introduces IFACE, a novel framework that unifies intrinsic geometry and chemical fields to compute a joint distance for protein surfaces, enabling more accurate separation of conformational variability from structural divergence and revealing conserved functional pockets within complex protein families.
This paper proposes a mathematical model of the curvature blindness illusion, attributing the perception of sinusoidal lines as angular zigzags to two complementary V1 mechanisms: the segmentation of contours by contrast polarity breaks and the straightening of segments due to orientation channel fragmentation.
This paper proposes a novel, biologically plausible spiking control framework based on the free energy principle, where neurons fire only to reduce internal free energy, achieving efficient and robust performance against both external and internal perturbations while offering new insights for neuromorphic hardware implementation.
This study demonstrates that the longitudinal progression of radiologic pleuroparenchymal fibroelastosis (PPFE), quantified via automated analysis of low-dose CT scans, independently predicts increased mortality and adverse respiratory outcomes in large lung cancer screening cohorts.
This study demonstrates that assuming a uniform generation time distribution in renewal equation models can lead to inaccurate estimates of the time-dependent reproduction number (Rt) in structured populations, and it proposes a methodology to correct for this mis-specification to improve public health decision-making.
This paper presents a compact dynamical mean-field theory for large networks of coupled phase oscillators that preserves $2\pi$-periodicity and disorder averaging to derive a self-consistent single-oscillator stochastic equation, successfully bridging microscopic phase response data with macroscopic synchronization predictions for arbitrary phase-reducible systems.
This paper introduces a filtration-based sequential learning framework that decomposes the impact of adding taxa on phylodynamic inference into learning, mismatch, and covariance components, revealing fundamental limits on what sequence data alone can reveal about latent genealogies due to irreducible gaps between analyst and oracle performance.
This paper introduces a biologically inspired learning mechanism where error signals diffuse locally through recurrent spiking neural networks, enabling effective temporal credit assignment and improved learning performance even with sparse feedback connectivity.
This paper presents a scalable Python-based algorithm that automates the identification and classification of homeostatic mechanisms in complex biological input-output networks by extending theoretical frameworks to handle multiple inputs and directly enumerating homeostatic subnetworks from connectivity structures, thereby overcoming the combinatorial and accessibility limitations of previous graph-theoretical approaches.
This paper proposes a non-Abelian gauge theory framework using the Doi-Peliti formalism to explain the rapid genetic radiation of *Yersinia pestis* and the existence of historical safe zones like Poland and Bohemia as mathematically necessary topological voids arising from the destructive interference of mutation-driven traveling waves.
This paper proposes a novel phase-amplitude coupling (PAC) detection framework based on nonlinear system identification and dynamical systems theory that overcomes limitations of existing methods by accurately characterizing coupling dynamics while remaining robust to noise, filter bandwidth variations, and harmonic-induced spurious couplings.
This paper argues that conserved quantities in mass-action networks are dual to internal cycles and proposes conjectured duality relationships between preclusters, maximal invariant polyhedral supports, and siphons.
This paper introduces a quantum mechanics-inspired interaction-picture framework to quantify the abscopal effect as a continuous, stochastic phenomenon in the context of PULSAR, enabling individual-level analysis of tumor interactions and standardized cross-study comparisons in preclinical models.
This study demonstrates that the choice of musical feature encoding fundamentally shapes network representations of music, revealing a critical trade-off where compressed single-feature models offer high uncertainty but low learning error, while richer multi-feature models preserve fine distinctions at the cost of increased state space complexity and higher model error, thereby determining how plausibly these networks reflect realistic listener expectations.