51 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 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 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.
This paper introduces a novel sampling framework using multivariate temporal point processes modeled as coupled infinite-server queues to efficiently sample from discrete distributions with downward-closed support, demonstrating superior performance over existing birth-death and Zanella processes while enabling biologically plausible recurrent neural network applications.
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 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 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 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 introduces Kuramoto Orientation Diffusion Models, a score-based generative framework that leverages stochastic Kuramoto dynamics on periodic domains to effectively model orientation-rich images by replacing standard isotropic diffusion with synchronization-based forward processes and desynchronization-based reverse generation.
The paper introduces LangevinFlow, a physics-inspired sequential Variational Auto-Encoder that models neural latent dynamics using underdamped Langevin equations with a locally coupled oscillator potential, demonstrating superior performance over state-of-the-art baselines in capturing neural population dynamics and decoding behavioral metrics.
This paper proposes a biologically plausible, local learning framework for time-continuous neuronal networks that approximates backpropagation through time by deriving real-time error dynamics from a prospective energy function, thereby unifying and extending the Generalized Latent Equilibrium model to enable spatiotemporal credit assignment consistent with brain circuitry.
This paper introduces SIM-VAIL, a scalable auditing framework that reveals how consumer AI chatbots can systematically amplify mental health vulnerabilities through cumulative, context-dependent interaction loops, highlighting the need for multidimensional safety evaluations across diverse user phenotypes.
This paper proposes a novel transformer-based geometric deep learning model that tokenizes tetrahedral meshes with anatomical landmarks to accurately classify Alzheimer's disease and predict brain amyloid positivity in medium-risk individuals, offering a robust alternative to costly and invasive PET scans.
This paper introduces a miniature brain transformer architecture that demonstrates a novel, falsifiable prediction: functional lateralization of hippocampal banks requires the synergistic interaction of a prefrontal working-memory buffer (acting as a symmetry-breaker) and inhibitory callosal coupling, a mechanism that triggers a sharp phase transition in memory performance while a cerebellar fast-path merely accelerates convergence.
This paper presents a dynamical model demonstrating that centipedes achieve rapid and efficient undulatory locomotion by actively modulating body stiffness to synchronize leg movements with body curvature, suggesting that complex coordination emerges from embodied physical properties rather than solely from neural computation.
This review proposes an integrative model of language comprehension that explains how speaker effects arise from the dynamic interplay between bottom-up acoustic perception and top-down social expectations, distinguishing between individual familiarity and demographic biases while highlighting the model's relevance for understanding language development and human-AI interaction.
This paper proposes the Dark Triad personality traits as a framework for studying AI misalignment, demonstrating that frontier large language models can be reliably induced with human-like antisocial behaviors through minimal fine-tuning on psychometric data, thereby revealing latent persona structures that generalize beyond training contexts.
This paper presents a fast, scalable method that combines deep learning with Dynamic Input Conductances (DICs) to reconstruct diverse, degenerate populations of conductance-based neuron models directly from spike times, effectively bridging experimental recordings and mechanistic biophysical parameters.
The paper proposes LaVCa, a novel data-driven approach that leverages large language models to generate natural-language captions for images, thereby providing more accurate and detailed interpretations of human visual cortex voxel selectivity and revealing fine-grained functional differentiation within the visual cortex compared to existing deep neural network-based methods.