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- Qlif Cast Quantum Spiking ForecastingQuantum Leaky-Integrate-and-Fire (QLIF-CAST) methodology for time-series forecasting. Adapts QLIF spiking neural networks for multivariate regression, achieving 15.4% lower MSE than classical LIF and 94% faster convergence than QLSTM/QNN. Activated by: quantum spiking forecasting, QLIF, time-series quantum, quantum regression.Votes: 0GitHub stars: 3
- Q Spirl Quantum Spiking RlQ-SpiRL: Quantum Spiking Reinforcement Learning framework combining spike-based temporal processing with variational quantum feature transformation for adaptive robot navigation and control. Use when: quantum reinforcement learning, spiking neural network RL, quantum spiking systems, robot navigation policies, quantum-enhanced SNN.Votes: 0GitHub stars: 3
- Psvit Structured Pruning Spiking VisionStructured pruning methodology for Spiking Vision Transformers (SViT) using uniform channel-wise filter pruning and sensitivity analysis for 22.4% memory savingVotes: 0GitHub stars: 3
- Population Templatebased Brain Graph Augmentation For Improving Oneshot Learning Classification**arXiv ID:** 2212.07790 **Authors:** Oben Özgür, Arwa Rekik, Islem Rekik **Published:** 2022-12-14T14:56:00Z **Abstract:** The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems. Previous studi...Votes: 0GitHub stars: 3
- Nm Pruning Spiking Neural NetworksProbability-based N:M semi-structured pruning for Spiking Neural Networks from scratch. Uses basis-logit parameterization and eligibility-inspired distillation (EID) for hardware-amenable sparsity patterns. Activation: N:M pruning SNN, SpikeNM, semi-structured spiking pruning, probability pruning SNN.Votes: 0GitHub stars: 3
- Neural Transfer UnificationNeural Transfer Unification (NTU) methodology for efficient foundation decoders in fault-tolerant quantum computing. Aligns decoding tasks across code distances via shared algebraic structures, enabling knowledge transfer from small to large code distances. Use when designing quantum error correction decoders, neural decoders for surface codes, or cross-distance knowledge transfer for quantum fault tolerance. Triggers: quantum decoder, foundation decoder, neural transfer, code distance scalin...Votes: 0GitHub stars: 3
- Neural Transfer Unification QecNeural Transfer Unification (NTU) framework for efficient foundation decoders in fault-tolerant quantum computing at large code distancesVotes: 0GitHub stars: 3
- Knowledge And Gradient Guided Reinforcement LearniKnowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes - In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic ac...Votes: 0GitHub stars: 3
- Humainsjunior A 38b Language Model Achieving Gpt4olevel Factual Accuracy By Directed Exoskeleton Reasoning**arXiv ID:** 2510.25933 **Authors:** Nissan Yaron, Dan Bystritsky, Ben-Etzion Yaron **Published:** 2025-10-29T20:12:36Z **Abstract:** We introduce Humans-Junior, a 3.8B model that matches GPT-4o on the FACTS Grounding public subset within a $\pm 5$ pp equivalence margin. Results. On Q1--Q500 under identical judges, GPT-4o scores 73.5% (95% CI 69.5--77.2) and Humans-Junior 72.7% (95% CI 68.7--76.5); the paired difference is 0.8 pp (bootstrap 95% CI $-3.1$ to $+4.7$; permutation $p = 0.72$; Co...Votes: 0GitHub stars: 3
- Graft Neural Population AdapterGRAFT methodology for Transformer-based neural population activity modeling with gain-recalibrated adapters enabling cross-day BCI recalibrationVotes: 0GitHub stars: 3
- Globally Optimal Snn Parameter ReconstructionGlobally optimal Spiking Neural Network (SNN) training via parameter reconstruction methodology. Extends convexification of parallel feedforward threshold networks to parallel recurrent threshold networks, enabling parameter reconstruction algorithm that avoids surrogate gradient approximation errors. Applicable to SNN training, optimization, energy-efficient neural networks. Triggers: SNN training, surrogate gradient, spiking neural network optimization, convex training, globally optimal SNN.Votes: 0GitHub stars: 3
- Event Based Neural Decoding NeuroprostheticThis skill summarizes the methodology from arXiv:2607.11445v1 "Event-based Neural Decoding for Neuroprosthetic Motor Control". The paper proposes a high-performance neural decoding method that balances task performance and efficiency using an event-based gated recurrent unit (GRU) generating sparse communication with graded spikes, enabling on-device neural decoding for neuroprosthetics.Votes: 0GitHub stars: 3
- Eeg Fuseformer Seizure PredictionTransformer-driven feature fusion framework for EEG-based seizure onset prediction. Combines CNN-LSTM (raw signal) + ResNet-18 (STFT) features via transformer encoder, achieves 98.85% recall on CHB-MIT dataset.Votes: 0GitHub stars: 3
- Contravariance Theory Strong Alignment Minimal SolutionsTheory formalizing contravariance in NeuroAI: weak alignment of network representations via affine mappings guarantees strong alignment of privileged axes, and alignment zippers up the network hierarchy. Shows convergent evolution between artificial and brain networks is inevitable for sufficiently hard tasks. Use when working with neuroai, brain-alignment, dnn-brain-comparison.Votes: 0GitHub stars: 3
- Arxiv 2608 20322 A Comparison Between Ceiling Mounted Fmcw Ir Uwb AA comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection (arXiv: 2608.20322)Votes: 0GitHub stars: 3
- Neuromorphic SpiNNaker AslNeuromorphic visual attention framework for sign language recognition on SpiNNaker hardware. Combines event-based vision sensors with spiking neural networks for energy-efficient real-time ASL recognition. Use when: deploying low-power gesture/sign recognition, implementing event-based vision on neuromorphic hardware, building SpiNNaker applications, designing energy-efficient computer vision systems, working with DVS (Dynamic Vision Sensor) data.Votes: 0GitHub stars: 3
- Neuromorphic Lidar Bev SnnEnd-to-end Spiking Neural Network for 3D LiDAR-based Bird's Eye View object detection in autonomous driving, with neuromorphic energy analysis and learned spike encoding.Votes: 0GitHub stars: 3
- Neuromorphic Continual Nuclear Ics神经形态持续学习方法用于核电厂工业控制系统(ICS)监测的顺序部署。结合脉冲神经网络(SNN)和在线学习，实现关键基础设施的实时异常检测和安全监控，同时防止灾难性遗忘。适用于关键基础设施保护、工业网络安全、边缘AI。Votes: 0GitHub stars: 3
- Neuroaps Net Alzheimer Point CloudNeuro-Anatomically Aware Point Cloud Representation (NeuroAPS-Net) for efficient Alzheimer's disease classification from MRI. Converts T1-weighted MRI into anatomically-informed 2D point clouds with region-aware feature encoding. Activation triggers: Alzheimer's classification, neuroanatomical point cloud, MRI analysis, geometric deep learning.Votes: 0GitHub stars: 3
- Neural Operator Adaptive Pde ControlDual-learning architecture combining online adaptive control with offline neural operator approximation for backstepping control of nonlinear hyperbolic PDEs with unknown Volterra series. Use when controlling PDE systems where gain computation is prohibitively expensive for real-time, particularly for fluid dynamics, traffic flow, or heat transfer systems.Votes: 0GitHub stars: 3
- Neural Inverse Design Scintillator MedicalNeural network inverse design of nanophotonic scintillators for medical imaging. Uses physics-informed neural networks to learn scintillation cascade processes from incident particles to photon emission, enabling end-to-end differentiable optimization of scintillator geometry. Use when designing scintillators for X-ray imaging, medical radiation detectors, or optimizing photon emission patterns via inverse design.Votes: 0GitHub stars: 3
- Neural Brain FrameworkNeuroscience-inspired framework for embodied AI agents. Use when building embodied agents, designing neural brain architectures, integrating multimodal sensing with cognition, implementing neuroplasticity-based memory systems, or optimizing neuromorphic hardware/software for real-world autonomous systems. Covers active sensing, perception-cognition-action loop, adaptive memory, and energy-efficient neuromorphic design.Votes: 0GitHub stars: 3
- Network Quantum Sensing CertificationCertification methodology for network quantum sensing — resolving the tension between quantum metrology and cryptography for distributed sensor networks. Framework for certifying security and precision guarantees simultaneously in noisy, insecure quantum networks.Votes: 0GitHub stars: 3
- Multi View O Information Brain NetworksHigher-order brain interaction analysis using O-information and Multi-View Information Bottleneck for fMRI-based psychiatric diagnosis. Decomposes multivariate neural interactions into redundant and synergistic components across multiple brain views for improved diagnostic classification. (arXiv:2604.17713, April 2026)Votes: 0GitHub stars: 3