Explore AI Agent Skills & Claude Prompts

Generalized framework of antisymmetric cross-polyspectral indices for identifying high-order neural interactions. Quantifies cross-frequency coupling while being intrinsically robust to volume conduction artifacts. Applicable to EEG/MEG analysis and personalized mTMS protocol design. Activation: antisymmetric polyspectral, cross-frequency coupling, high-order neural interactions, volume conduction robust, bispectral analysis, trispectral analysis, multi-frequency coupling, mTMS protocol.

l-system-neural-network-evolution

L-System genetic encoding methodology for scalable neural network evolution. Uses Lindenmayer system grammar to encode neural networks, enabling compact representation and efficient evolutionary search. Applies to: neuroevolution, scalable network encoding, genetic algorithms, neural architecture search. Activation: L-system neural encoding, Lindenmayer neuroevolution, genetic network encoding, scalable neural evolution, grammar-based NAS.

l-spine-simd-snn-engine

L-SPINE low-precision SIMD spiking neural compute engine with unified multi-precision datapath (INT2/4/8). Multiplier-less shift-add model for FPGA-based edge SNN inference with 3 orders of magnitude energy efficiency improvement. Activation: L-SPINE, SIMD SNN, low-precision SNN, FPGA SNN inference, shift-add SNN

l-spine-snn-compute-engine

L-SPINE 低精度 SIMD 脉冲神经计算引擎方法论。用于资源受限边缘设备的高效 SNN 推理，支持 2/4/8-bit 多精度数据通路，无乘法器 shift-add 模型。适用于神经形态硬件设计、边缘 AI 部署、FPGA SNN 加速。触发词: l-spine, snn hardware, edge inference, low-precision snn, spiking neural compute engine

lilac-safe-continual-rl

LILAC+ — Safe continual RL under nonstationarity with adaptive safety constraints (context-based, adaptation-speed, budget-to-state).

face-perception-inverse-generative

Human face perception methodology using controversial stimulus pairs to distinguish between theoretically distinct DNN models. Shows that human face perception is shaped by inverse-generative mechanisms that infer latent causes of facial appearance and discount nuisance variation, tuned by natural image statistics. arXiv:2605.12619.

inverse-engineering-quantum-control

Inverse engineering methodology for quantum control in multi-ion devices. Design control protocols for classical piston dynamics driven by quantum motion. From arXiv:2606.03488 (Li, Sherman, Ruschhaupt, 2026).

inverse-born-rule-fallacy

Critical analysis methodology for quantum data encoding — identifies how naive amplitude encoding (psi=sqrt(P)) abelianizes the Hilbert space and fails to achieve genuine quantum advantage in QML/finance. Advocates for Dynamical Hamiltonian Encoding (DHE) where data generates non-commutative evolution.

schedule Updated 17 days ago

functional-whole-brain-models

Functional Whole-Brain Models (FWBM) methodology bridging bottom-up whole-brain modeling and top-down neuroconnectionism. Combines biophysically detailed simulations with functional-performance-driven deep neural networks. Use when: designing brain-scale computational models, integrating structure and function in neural modeling, building neuroconnectionist models with biological grounding, developing hybrid brain models that achieve both biological fidelity and functional competence. Activation: whole-brain modeling, neuroconnectionism, functional brain model, brain simulation, WBM, FWBM, biophysically detailed brain, brain DNN integration, brain foundation model, brain dynamics modeling. Based on arXiv:2605.18118 (May 2026).

tensor-network-neurological-predictor

Tensor Network Feature Engineering methodology for multi-class neurological disorder prediction from MRI data. Uses tensor network decompositions to extract high-dimensional features from sparse medical imaging. Activation: tensor network MRI, neurological disorder prediction, tensor feature engineering, multi-class brain disorder, MRI tensor decomposition.

explainable-gnn-eeg-neurological

Explainable GNN for EEG Neurological Evaluation

foundation-models-brain-biomarker

Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity. Use when: building neurological biomarker discovery pipelines, applying foundation models to fMRI/EEG data, analyzing dynamic functional connectivity for disease detection, developing robust cross-subject biomarkers. Triggers: brain biomarker foundation model, dynamic functional connectivity biomarker, neurological disorder detection, robust biomarker discovery, fMRI foundation model.