hiyenwong/ai_collection - Page 34

neuromorphic-supremacy

Neuromorphic Supremacy methodology — hybrid astrocytic-spiking neural architectures that outperform classical deep learning in noisy, data-scarce environments

neuron-dropin-neuroplasticity

Neuron-level DropIn and neuroplasticity mechanisms for enhancing deep learning efficiency and performance. Addresses the bottleneck of parameter scaling by enabling targeted neuron replacement and adaptive plasticity.

neuron-model-reconstruction

从锋电位时间序列快速重构电导神经元模型。 结合深度学习和动态输入电导（DIC）框架，解决神经元简并性问题。 触发词：神经元模型、电导模型、锋电位、模型重构、DIC、 conductance-based model, spike times, neuron reconstruction, degeneracy。

Source text: Mixed languages

neuron-photonic-spiking-laser

Photonic spiking neurons using multi-junction VCSELs (NeuronSEL) with negative differential resistance. Neuromorphic photonics for ultra-fast optical information processing. Triggers: photonic neuron, VCSEL, neuromorphic photonics, spiking laser, optical…

neuron-surface-emitting-laser-neuronsel-spiking

Methodology from paper 'Neuron Surface Emitting Laser (NeuronSEL): Spiking Regimes and Negative Differential Resistance in S...' by Maria Duque-Gijon et al. (2026-04-14). Activation: brain, neural, spiking, neuron, network, physics.optics

neuronal-arithmetic-ots

Neuronal arithmetic operators using Ovonic Threshold Switches (OTS) for biologically inspired analog computing. Implements additive integration and divisive gain modulation through synaptic conductance changes and shunting inhibition. Trigger words: Ovonic…

neuronal-murburn-thermodynamic-electricity

Murburn thermodynamic framework for neuronal electrical activity - unified reaction-transport-relaxation model explaining resting potential, excitability, and signal propagation. Activation triggers: murburn, neuronal electricity, electron holding potential,…

neuropinns-spiking-pinn

NeuroPINNs methodology — neuroscience-inspired Physics-Informed Neural Networks using Variable Spiking Neurons for energy-efficient PDE solving

Source text: Mixed languages

neuroplastic-plasticity-optimizer

NeuroPlastic - A plasticity-modulated optimizer for biologically inspired learning dynamics. Incorporates synaptic plasticity mechanisms like Hebbian learning, homeostatic plasticity, and metaplasticity into gradient-based optimization for enhanced learning…

neuroring-multi-fpga-snn

NeuroRing modular and scalable SNN accelerator based on multi-FPGA bidirectional ring topology and stream-dataflow architecture. Use when scaling Spiking Neural Networks (SNN) across multiple FPGAs; implementing event-driven neuromorphic hardware; designing…

neuroring-multifpga-snn

NeuroRing methodology for modular and scalable SNN accelerator based on multi-FPGA bidirectional ring topology and stream-dataflow architecture

neuroscience-frontiers-2026

Comprehensive synthesis of cutting-edge neuroscience and NeuroAI research from 2025-2026. Covers NSF NeuroAI workshop findings, CogniSNN random graph architectures, EMBER hybrid cognitive systems, SpikingBrain2.0 foundation models, and Next Generation Neural…

neuroscience-graph-operators-virtual-sensing

Neuroscience-inspired graph neural operators for edge-deployable virtual sensing on irregular geometries. Enables sparse-to-dense reconstruction and real-time full-field physics prediction with latency and energy constraints.

nl-cps-kubernetes-control

Reinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters

nn-quantum-state-encoding

Neural network encoding methodology for quantum state preparation: trains classical neural network to map input data directly to quantum circuit parameters, avoiding per-instance variational optimization. Achieves 0.992 fidelity on unseen data with 5000x…

noise-directed-adaptive-remapping

Noise-directed adaptive remapping methodology for integer optimization — encoding qubit-based problems into qudit representations with noise-aware adaptation. Use when optimizing quantum integer optimization on NISQ hardware, converting qubit encodings to…

noise-enhanced-quantum-kernels

Noise-enhanced quantum kernel methods for analog quantum computing. Implements analog and hybrid quantum kernels with noise-induced performance improvements for quantum machine learning. Activation: noise quantum kernel, analog quantum kernel, quantum kernel…

non-unitary-qml-fisher-efficiency

Non-unitary quantum machine learning via Linear Combination of Unitaries (LCU) framework, with Fisher efficiency transitions and threshold-dependent parameter scaling in medical imaging tasks. Use when: implementing non-unitary quantum layers, benchmarking…

nonlinear-mas-optimal-control

Nonlinear Multi-Agent Systems Optimal Control - 非线性多智能体系统分布式最优控制。核心技术：HJB方程分布式近似、私有信息结构、保密协作控制。激活词：MAS optimal control, multi-agent control, 非线性最优控制, HJB distributed.

Source text: Chinese

nuclear-lattice-vqe

Variational Quantum Eigensolver (VQE) framework for nuclear lattice effective field theory. Computes ground state energies of light nuclei (2H, 3H, 4He) using Gray code encoding with symmetry reduction for compact qubit representation. Keywords: nuclear…

off-switch-dual-use-gram

Gradient-Routed Auxiliary Modules (GRAM) methodology from Anthropic/AE Studio research (Jul 8, 2026) — training a single LLM with removable, category-specific knowledge compartments that can be toggled on/off post-training without retraining, enabling…

omni-sleep-foundation

Omni-Sleep sleep foundation model methodology using CNS/ANS hierarchical contrastive learning for topology-constrained multimodal PSG representation learning. Use when working with sleep staging, affective BCI, polysomnography analysis, CNS-ANS dynamics,…

one-shot-quantum-signatures

Quantum algorithm for one-shot signatures using affine coset superposition and puncturable PRFs. Provides circuit-level implementation for delegated signatures, secured token transfer, and publicly verifiable randomness. Use when implementing quantum…

opencode

Open source AI coding agent with multi-agent orchestration and ultrawork mode. Use when user mentions opencode, open code, oh-my-opencode, ultrawork, ulw, or needs an AI coding agent with background tasks and LSP integration.

openspec

Specification-driven development framework using Gherkin syntax. Use when user mentions openspec, open spec, spec-driven, gherkin, BDD, given-when-then, or needs to define requirements in structured human-readable format.

optimal-ansatz-free-hamiltonian-learning

Optimal ansatz-free Hamiltonian learning methodology — control-free, ancilla-free algorithm using randomized-sampling framework with band-limited kernel-based time sampling and displacement sieve for Hamiltonian structure learning. Use for quantum device…

organic-magnetic-field-free-quantum

Magnetic-field-free quantum computing and quantum reservoir computing framework using engineered organic materials based on the 3-Layer Quantum Brain Hypothesis. Covers SVILC qubits, CQEC error correction, and four implementation paths. Use when: organic…

hybrid-quantum-classical-reservoirs

Hybrid quantum-classical reservoir computing (HRC) combining qubit quantum reservoir with classical echo state network for nonlinear functional approximation and temporal processing of quantum states. Outperforms standalone components in both linear and…

non-markovian-kerr-feedback-qrc

Proves unbounded computational superiority of Kerr nonlinear feedback over Gaussian linear reservoirs in continuous-variable quantum reservoir computing. Single Kerr mode with feedback depth D replaces up to ~100 linear modes. Use when: CV-QRC design,…

dynamic-neural-manifolds-snn-control

Dynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware — using circuit mechanisms (heterogeneous inhibition, gain modulation, transient currents) as control knobs for manifold geometry, enabling explainable,…

adaptive-spiking-neuron-multimodal

Adaptive Spiking Neuron (ASN) methodology for energy-efficient vision and language modeling. Features trainable membrane potential dynamics, adaptive firing thresholds, integer training with spike inference paradigm, and variance-invariance loss for…

agentic-fusion-materials

Agentic AI framework for materials discovery that synergizes Large Atomic Models (LAMs) with Large Language Models (LLMs). Use for designing autonomous materials discovery pipelines, integrating atomic-scale numerical computation with semantic reasoning, and…

agentic-portfolio-management

Agentic AI framework for portfolio management using multi-agent collaboration, competitive method evaluation, and meta-learning. Implements the architecture from 'The Self Driving Portfolio' paper (arxiv 2604.02279). Use when: (1) Building automated…

ai-sycophancy-measurement

Methodology for measuring, analyzing, and mitigating AI sycophancy in guidance-giving contexts. Covers automated classification, stress-testing with prefilling, synthetic data generation, and domain-specific analysis.

analog-quantum-event-gnn

Analog quantum AEGNN methodology — implementing event-based graph neural networks on neutral-atom quantum processors using Rydberg Hamiltonians for message passing.

arxiv-search

arXiv paper search skill - search academic papers by keywords, authors, categories. Supports time filtering, category filtering, and paper detail retrieval. Activation: arxiv search, paper search, 论文搜索, search papers, arxiv 论文.

behavior-vlm-neuroscience

Finetuning-free behavioral understanding framework for neuroscience using vision-language models. Enables pose estimation and behavioral analysis linking neural activity to natural actions without human annotation. Use when: analyzing animal behavior from…

boosting-brain-to-image-tribe-v2

TRIBE v2 data augmentation methodology for brain-to-image decoding. Uses pretrained encoding model on 1000+ hours of video/audio/language fMRI to generate synthetic data, achieving 68% improvement in Top-10 retrieval accuracy. Supports zero-shot decoding when…

brain-cliplm-semantic-compression-eeg

Brain-CLIPLM semantic compression framework for EEG-to-text decoding. Two-stage methodology: semantic anchor recovery via contrastive learning + anchor-guided sentence reconstruction with retrieval-grounded LLM. Key principle: granularity matching - aligns…

brain-connectivity-analysis

Brain network connectivity analysis using knowledge graph tools. Analyze brain connectivity patterns, neural networks, and graph-based brain models. Use when working with brain graphs, connectivity matrices, neural network analysis, or integrating…