Research

Research, evidence gathering, literature, reports, investigation, and synthesis

skills in category: 21,409
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Showing 11,305–11,328 of 21,409 skills

Neural Dynamics Decision MakingANeural Dynamics Decision-Making ModelsresearchpythonVotes: 0GitHub stars: 3
Neural Dynamics Analysis MethodologyAComprehensive framework for neural dynamics analysis integrating multiple methodologies: (1) Neural population decoding and encoding, (2) Brain network dynamics modeling, (3) Neural criticality assessment, (4) Spiking neural network dynamics, (5) Brain-connectome computational analysis. Use when studying neural system dynamics, brain network evolution, neural population behavior, or implementing computational neuroscience models.researchpythongoVotes: 0GitHub stars: 3
Neural Digital Twins BciANeural Digital Twins framework for Brain-Computer Interfaces (BCIs). Addresses neuroplasticity-induced recalibration, session-to-session variability, and real-time adaptation through personalized brain models.researchpythongitVotes: 0GitHub stars: 3
Neural Computation Without SlotsABiologically plausible memory and attention without dedicated storage slots. Extends Modern Hopfield Networks (MHN) to K-winner ensembles for improved continual learning retention, and demonstrates MHN can capture slot-based memory functions of LLMs. Activation: memory without slots, biologically plausible attention, K-winer Hopfield network, ensemble memory MHN, McClelland memory model.researchgoperformanceVotes: 0GitHub stars: 3
Neural Code SpeakAAutomated characterization of individual neurons through natural language using generative models and neural digital twins. Use when: studying neuron selectivity in visual cortex, building closed-loop frameworks for neural characterization, generating semantic hypotheses for neural tuning, or doing automated neuron description via vision-language models.researchtestinggitVotes: 0GitHub stars: 3
Neural Code Language CharacterizationAClosed-loop framework for automated neuron characterization using natural language descriptions. Translates neuron activation patterns into semantic hypotheses, verifies them via in silico experiments using neural digital twins. Use when: neuron selectivity analysis, neural code interpretation, automated neuroscience discovery, V1/V4 characterization, digital twin experiments, semantic description of neurons, generative model for neuroscience.researchgotestingVotes: 0GitHub stars: 3
Neural Behavioral Whole Body Movement MonkeysANeural-behavioral representation framework for natural whole-body movement in primates. Combines large-scale epidural cortical signals with synchronized multi-view motion capture to decode unconstrained whole-body kinematics. Use when: (1) decoding natural whole-body movements, (2) modeling neural-behavioral representations, (3) primate motor neuroscience research, (4) developing behavior priors for movement decoding. Keywords: whole-body movement, motor decoding, primate neuroscience, neural...researchpythonVotes: 0GitHub stars: 3
Naturalistic Computational Cognitive ScienceAFramework for building generalizable cognitive science models using naturalistic experimental paradigms and AI integration. Argues that naturalistic stimuli/tasks elicit distinct neural and behavioral patterns not captured by controlled experiments. Use when: designing ecologically valid neuroscience experiments, integrating AI models with cognitive science, naturalistic fMRI/behavioral studies, computational cognitive modeling, generalization of neural findings. Triggered by: naturalistic co...researchgotestingVotes: 0GitHub stars: 3
Native Active Perception ReasoningANative active perception methodology for omni-modal understanding using POMDP-based Observation-Thought-Action cycle, Agentic Supervised Fine-Tuning (ASFT), and TAURA turn-level credit assignment.researchpythonperformanceVotes: 0GitHub stars: 3
Mzeqas Zero Shot Quantum NasAZero-shot quantum neural architecture search methodology using QNTK convergence and MCTS for VQA circuit design. Eliminates repeated training costs. Activation: quantum NAS, neural architecture search, zero-shot, VQA, MCTS, 量子架构搜索.researchgoexpressVotes: 0GitHub stars: 3
Multiview Brain Network Foundation ModelAMV-BrainFM: Cross-view consistency learning for multi-view brain network foundation models. Activation: multi-view learning, brain networks, foundation models.researchpythongoVotes: 0GitHub stars: 3
Multiscale Brain Dynamics AnalysisAUnified framework for multi-scale brain dynamics analysis combining criticality scaling, fixed point compositionality, and representation diagnostics. Integrates renormalization group methods, inhibition-dominated network theory, and EEG foundation model audit protocols.researchVotes: 0GitHub stars: 3
Multi Timescale Conductance Spiking NetworksAMulti-Timescale Conductance (MTC) Spiking Networks — gradient-trainable framework with rich firing dynamics for enhanced temporal processing. Conductance-based neuron model with fast/slow/ultra-slow timescales enables tonic, phasic, and bursting responses within a single model. Trainable via standard BPTT without surrogate gradients. Activation: multi-timescale conductance, MTC spiking network, conductance-based neuron, spiking neural network regression, surrogate-free SNN training, I-V curve...researchgogitVotes: 0GitHub stars: 3
Multi Source Fmri TaskonomyAMulti-source fMRI cognitive taskonomy framework using transfer learning across 23 HCP task states. Extends single-source to many-to-one task relations with Boolean Integer Programming for budget-constrained task allocation. Activation: fMRI taskonomy, cognitive tasks, transfer learning, multi-source, HCP, BIP.researchpythontestingVotes: 0GitHub stars: 3
Multi Scale Hypergraph Brain ConnectivityAMulti-scale hypergraph learning (MuHL) methodology for high-order brain connectivity analysis beyond pairwise GNNs. Accepted to ICML 2026. Use for: brain network analysis, neurodegenerative disease classification (Alzheimer's, Parkinson's), higher-order functional connectivity, hypergraph neural networks.researchnodeperformanceVotes: 0GitHub stars: 3
Multi Objective Snn OscillationAMulti-objective genetic algorithm (NSGA-III) optimisation of spiking neural networks (RSNNs) to match neural firing rates and oscillation frequencies for computational neuroscience modeling.researchpythongoVotes: 0GitHub stars: 3
Multi Objective Optimisation Oscillatory SnnAMulti-objective genetic algorithm (NSGA-III) optimisation of Izhikevich neuron-based recurrent spiking neural networks for simultaneously matching neural firing rates and network oscillation frequencies. Based on arXiv:2605.25224 (May 2026). Use when studying SNN parameter fitting, neural oscillations, genetic algorithm optimisation for spiking networks, or brain organoid modeling.researchgoVotes: 0GitHub stars: 3
Multi Ensemble Mean Field OscillatorsAMulti-ensemble mean-field reduction for networks of globally coupled phase oscillators with arbitrary (empirical) frequency distributions. Extends Ott-Antonsen to heterogeneity beyond Lorentzians. arXiv:2607.09516researchgonodeVotes: 0GitHub stars: 3
Webswarm Recursive Multi Agent Orchestration For Deep AndAWebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search. Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ... Activation: agent, multi-agent, agentic, llm, orchestrationresearchgoreactVotes: 0GitHub stars: 3
War Workload Aware Rollouts Synchronous Agentic Reinforcement LearningASkill derived from arXiv:2607.17299 - WAR: Workload-Aware Rollouts for Synchronous Agentic Reinforcement LearningresearchVotes: 0GitHub stars: 3
Variance Reduction For Policy Gradient With ActionASkill for AI agent capabilitiesresearchpythongoVotes: 0GitHub stars: 3
Unveiling The Decisionmaking Process In Reinforcement Learning With Genetic ProgrammingA**arXiv ID:** 2407.14714 **Authors:** Manuel Eberhardinger, Florian Rupp, Johannes Maucher, Setareh Maghsudi **Published:** 2024-07-20T00:45:03Z **Abstract:** Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions. Incomprehensibility, however, is not an option for the use of (deep) reinforcement learning in the real world, as unpredictable actions can seriously harm the involved individuals. In this work, we propose a genetic programmin...researchperformanceVotes: 0GitHub stars: 3
Trim Reducing Ai Generated Codeslop Agent Trajectory MinimizationASkill derived from arXiv:2607.18161 - TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory MinimizationresearchVotes: 0GitHub stars: 3
Towards Robust And Domain Agnostic Reinforcement Learning CompetitionsA**arXiv ID:** 2106.03748 **Authors:** William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie Wu, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute **P...researchgoVotes: 0GitHub stars: 3