51 papers
This study introduces Equilibrium-Informed Neural Networks (EINNs), a novel deep learning approach that reverses the traditional parameter-search process by inferring system parameters from candidate equilibrium states to efficiently detect critical bifurcation thresholds and tipping points in complex nonlinear dynamical systems.
This paper establishes a principled dynamical theory for sequential retrieval in input-driven Hopfield networks by deriving explicit mathematical conditions for self-sustained memory transitions within a two-timescale architecture, thereby bridging classical associative memory models with modern reasoning systems.
This paper presents an information-theoretic framework that optimizes experimental stimulus distributions to maximize the distinguishability between competing probabilistic neural coding hypotheses (likelihood vs. posterior encoding) by quantifying the expected performance gap between their respective decoders.
This paper introduces a biologically plausible, two-phase quadratic integrate-and-fire neuron model that eliminates unphysical voltage divergence while retaining an exact, analytically tractable low-dimensional description for neuronal ensembles.
This paper argues that adversarial examples arise from an exponential misalignment between the high-dimensional perceptual manifolds of neural networks and human concepts, suggesting that achieving robustness requires aligning these dimensions to match human perception.
This paper proposes a memory-augmented transformer where inhibitory cross-talk between lateralized memory banks enables functional specialization, significantly improving episodic recall while preserving rule-based prediction capabilities, whereas excitatory coupling leads to a collapse of this specialization.
This paper introduces NeuroFlowNet, a novel cross-modal generative framework based on Conditional Normalizing Flow that successfully reconstructs high-fidelity intracranial EEG signals from the deep temporal lobe using non-invasive scalp EEG, effectively overcoming the limitations of traditional methods in capturing complex brain dynamics and avoiding pattern collapse.
The paper introduces NEURONA, a neuro-symbolic framework that integrates symbolic reasoning with fMRI grounding to decode interacting visual concepts, demonstrating that incorporating structural priors significantly enhances both decoding accuracy and generalization to unseen queries.
The paper introduces SemKey, a novel framework that decouples semantic guidance into four objectives and redesigns EEG-LLM interactions to eliminate hallucinations and the BLEU trap, achieving state-of-the-art performance in EEG-to-text decoding through rigorous signal-grounded evaluation.
The paper introduces DecNefSimulator, a modular and interpretable framework that utilizes latent variable generative models to simulate decoded neurofeedback dynamics, enabling researchers to reproduce empirical learning phenomena, identify failure conditions, and optimize protocol designs in silico before human implementation.
This study demonstrates that incorporating biologically motivated, state-dependent synaptic feedback into a Lipkin-Meshkov-Glick quantum brain model significantly reshapes its phase diagram by expanding the paramagnetic phase and displacing critical boundaries, a phenomenon rigorously characterized through ground-state Husimi distributions, Wehrl entropy, and mean-field dynamical analysis.