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- Hybrid Tensor Network QmlHybrid tensor network architecture for quantum machine learning using post-selection as a trainable hyperparameter. Interpolates between classical and quantum tensor network edge cases by controlling quantum constraint enforcement via post-selection allocation. Use when designing hybrid quantum-classical ML models, tensor network quantum ML, or optimizing quantum resource allocation with limited post-selection budget. Activation: hybrid tensor network, quantum-classical interpolation, post-se...Votes: 0GitHub stars: 3
- Hybrid Intelligent Mental Health AssessmentMulti-dimensional mental health assessment using hybrid intelligent frameworks combining clinically validated screening tools, cognitive evaluation, and personality profiling with AI-driven decision support.Votes: 0GitHub stars: 3
- Hybrid Biophysical Neuron Models Neural OdesLearning Hybrid Biophysical Neuron Models with Neural ODEs — combining mechanistic biophysical models with machine learning for accurate and efficient neuron dynamics modelingVotes: 0GitHub stars: 3
- Human Like Object GroupingBehavioral benchmark and object-centricity analysis for self-supervised vision transformers. Uses two-dot same/different judgment task with 1020+ trials to measure human object grouping, and proposes Gram matrix alignment as a mechanism for improving behavioral alignment. Use when: vision transformer evaluation, object segmentation, self-supervised learning, DINO models, behavioral neuroscience benchmarks, Gram matrix distillation, human-AI visual alignment, psychophysics.Votes: 0GitHub stars: 3
- Hubo Quantum OptimizationHigher-Order Unconstrained Binary Optimization (HUBO) methodology for quantum optimization workflows. Compact binary encoding reduces qubit requirements vs QUBO but increases circuit depth via higher-order interaction terms. Use when formulating industrial logistics, scheduling, routing, or portfolio optimization problems for quantum/hybrid quantum-classical solvers, or when analyzing qubit-vs-depth trade-offs in HUBO vs QUBO encodings. (arXiv: 2605.30252)Votes: 0GitHub stars: 3
- Hqnn Expressibility TrainabilityExpressibility-trainability trade-off analysis and multi-objective NAS framework for Hybrid Quantum Neural Networks (HQNNs) — reveals how classical components reshape quantum optimization landscapes and decouple trainability from PQC expressibility.Votes: 0GitHub stars: 3
- Holobrain Holograph Oscillatory GnnHoloBrain and HoloGraph framework: modeling brain rhythms through coupled oscillatory synchronization and applying this principle to graph neural networks. Addresses GNN over-smoothing and enables reasoning on graphs through oscillatory dynamics.Votes: 0GitHub stars: 3
- Hippocampal Data Augmentation GeneralizationData augmentation framework for modeling hippocampal contributions to generalization across offline and online timescales. Use when implementing hippocampal-inspired AI systems or studying neural mechanisms of flexible repurposing of prior experiences.Votes: 0GitHub stars: 3
- Hippo Multi Attractor MemoryBiologically detailed extension of Hopfield/Marr auto-associative memory model for CA3 hippocampus. Implements ten populations (two asymmetric pyramidal subtypes, eight GABAergic interneurons) to study multi-attractor dynamics and stability effects in memory circuits.Votes: 0GitHub stars: 3
- Hierarchical Critical Brain DynamicsHierarchical organization of critical brain dynamics. Analysis of how brain structure hierarchies interact with criticality hypothesis. Activation: hierarchical brain, critical dynamics, connectome hierarchy, brain criticality.Votes: 0GitHub stars: 3
- Hierarchical Control GaasHierarchical control synthesis for continuous-time systems using epsilon-general Approximate Alternating Simulation (epsilon-gAAS) relations. Enables formal controller design with coarser abstractions while maintaining correctness guarantees. Use when: (1) designing hierarchical controllers for continuous-time systems, (2) building formal abstractions for complex dynamical systems, (3) synthesizing safety-critical controllers with correctness guarantees, (4) applying simulation relations for ...Votes: 0GitHub stars: 3
- Hierarchical Connectome PhcParallelized Hierarchical Connectome (PHC) framework for spatiotemporal recurrent spiking neural networks. Upgrades State-Space Models (SSMs) into spatiotemporal networks with biological constraints including Dale's Law, short-term plasticity, and reward-modulated STDP. Activation: spiking neural networks, SSM, connectome, spatiotemporal modeling, biological neural networks.Votes: 0GitHub stars: 3
- Hierarchical Brain CriticalityHierarchical organization of critical brain dynamics. Studies how criticality signatures vary along anatomical hierarchy in brain systems using phenomenological renormalization group approaches on large-scale neuronal spiking data.Votes: 0GitHub stars: 3
- Heterophily Synergistic InterdependenciesHeterophily as a generative mechanism for self-organized synergistic interdependencies in adaptive networks. Explains how heterophily induces higher-order dependencies while weakening pairwise dependencies, enabling robust collective behavior. Trigger words: heterophily, synergistic interdependencies, adaptive networks, higher-order dependencies, self-organization, network dynamics, collective behavior.Votes: 0GitHub stars: 3
- Heteroclinic Cognitive State ModelingModeling sequential cognitive states via population-level cortical dynamics using Universal Approximation Theorem to approximate heteroclinic cycles with neural field systems. Activation: heteroclinic cognitive states, sequential brain dynamics, Lotka-Volterra neural model, Amari neural field approximation, meditation state transitions.Votes: 0GitHub stars: 3
- Zeta Law Discoverability BiomedicalResearch skill for the paper "How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data" (arXiv:2604.17581) by Paul M. Thompson. Covers the Zeta Law framework derived from Riemann zeta function properties that characterizes how discovery probability scales with sample size in biomedical data. Applicable to sample size estimation, power analysis, discoverability modeling, brain connectomics, data collection planning, zeta function applications, and resource allocation in neur...Votes: 0GitHub stars: 3
- Zeta Law Biomedical ScalingZeta Law framework for predicting data scaling in biomedical discovery. Uses spectral covariance structure and Riemann zeta function to model cross-modal discoverability, predicting when models transition from underparameterized to overparameterized regimes. Activation: zeta law, biomedical data scaling, cross-modal discoverability, Riemann zeta function, scaling laws, data efficiency.Votes: 0GitHub stars: 3
- Synaptic Plasticity Models And Bioinspired Unsupervised Deep Learning A Survey**arXiv ID:** 2307.16236 **Authors:** Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato **Published:** 2023-07-30T13:58:46Z **Abstract:** Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are...Votes: 0GitHub stars: 3
- Simple Recurrent Neural Networks Is All We Need For Clinical Events Predictions Using Ehr Data**arXiv ID:** 2110.00998 **Authors:** Laila Rasmy, Jie Zhu, Zhiheng Li, Xin Hao, Hong Thoai Tran, Yujia Zhou, Firat Tiryaki, Yang Xiang, Hua Xu, Degui Zhi **Published:** 2021-10-03T13:07:23Z **Abstract:** Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR data, a patient's history is often represented as a sequence of visits, and each visit contains multiple events. A...Votes: 0GitHub stars: 3
- Reliable Mechanistic Operator Recovery With Biologically Informed NeuralMany biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data. Biologically-informed neural networks (BINNs. Based on arXiv:2607.07425.Votes: 0GitHub stars: 3
- Pa Tcnet Brain Tumor SegMulti-stage brain tumor segmentation using Pathology-Aware Temporal Calibration (PA-TCNet) with physiological consistency constraints across temporal sequences for biologically plausible predictions.Votes: 0GitHub stars: 3
- Neurrator Single Cell Semantic NarrationNEURRATOR - Semantic narration of vision at single-cell resolution. Maps spiking activity to natural-language descriptions via CLIP-LLaVA embedding space, enabling functional probing of cell types and brain regions.Votes: 0GitHub stars: 3
- Multi Vqc HealthcareMulti-VQC approach for healthcare classification using variational quantum circuits to address class imbalance in medical datasets.Votes: 0GitHub stars: 3
- Mediq Gan Medical Image GenerationQuantum-inspired GAN methodology for high-resolution medical image generation with prototype-guided skip connections and dual-stream generator. Addresses data scarcity, class imbalance, and privacy constraints in medical imaging through variational quantum circuits that preserve full-rank mappings and avoid rank collapse. Use when building quantum-inspired generative models for medical image augmentation, designing GAN architectures that balance expressivity with trainability, or analyzing la...Votes: 0GitHub stars: 3