Research

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

skills in category: 21,402
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Showing 10,345–10,368 of 21,402 skills

Dual Timescale Memory Snn AstrocyteADual-timescale memory in spiking neuron-astrocyte networks for efficient navigation. Combines long-term memory of successful actions with short-term suppression of recently visited locations using astrocyte-mediated modulation. Use when implementing bio-inspired navigation, working memory in SNNs, or astrocyte-neuron interactions. Triggers: dual timescale memory, astrocyte SNN, spiking navigation, neuron-astrocyte network, efficient exploration.researchpythonVotes: 0GitHub stars: 3
Dic Neuron Reconstruction Spike TimesADeep learning + Dynamic Input Conductances (DICs) methodology for fast reconstruction of degenerate conductance-based neuron populations from spike times alone, enabling scalable and interpretable inference from experimental recordings.researchgoVotes: 0GitHub stars: 3
Dendritic In Context Learning SnnADendriCL methodology for in-context learning in single-layer spiking neural networks using dendritic compartment dynamics. Use when: implementing ICL in biologically-plausible SNNs, designing compartmental spiking architectures, studying online LMS in dendrites, or building seed-stable ICL at super-dimensional task complexity. arXiv: 2607.02289researchgoapiVotes: 0GitHub stars: 3
Dendritic Icl SnnADendriCL methodology for dendritic in-context learning in single-layer spiking neural networks. Demonstrates that a single dendritic compartment with online-LMS dynamics is sufficient for general-purpose ICL without attention, depth, or inference-time plasticity.researchgoapiVotes: 0GitHub stars: 3
Combining Convolution Delay Learning Recurrent SpikingACombining convolutional recurrent connections with DelRec delay learning in spiking neural networks for resource-constrained edge deployment - arXiv:2604.15997 (April 2026). Covers convolutional recurrent SNNs, axonal delay learning, ~99% parameter reduction, 52x inference speedup on audio classification.researchpythongoVotes: 0GitHub stars: 3
Collapse Or Preserve Datadependent Temporal Aggregation For Spiking Neural Network AccelerationA**arXiv ID:** 2603.13810 **Authors:** Jiahao Qin **Published:** 2026-03-14T07:30:22Z **Abstract:** Spike sparsity is widely believed to enable efficient spiking neural network (SNN) inference on GPU hardware. We demonstrate this is an illusion: five distinct sparse computation strategies on Apple M3 Max all fail to outperform dense convolution, because SIMD architectures cannot exploit the fine-grained, unstructured sparsity of i.i.d. binary spikes. Instead, we propose Temporal Aggregated Con...researchVotes: 0GitHub stars: 3
Cognisnn Random GraphACogniSNN - Cognition-aware Spiking Neural Networks with Random Graph Architecture. Brain-inspired SNN with neuron-expandability, pathway-reusability, and dynamic-configurability. Use when implementing energy-efficient SNNs, neuromorphic computing, continual learning in SNNs, or brain-inspired neural architectures. Activation: CogniSNN, spiking neural network, SNN, random graph, brain-inspired, neuromorphic, continual learning, energy-efficient neural network, spike-based learning.researchpythontestingVotes: 0GitHub stars: 3
Clockless Neuromorphic ChipAScalable neuromorphic computing from autonomous spiking dynamics in clockless (asynchronous) reconfigurable FPGA chips. Boolean spiking neurons with configurable E/I weights, spike-encoded data pipeline, and competitive audio classification at significantly lower power.researchpythongoVotes: 0GitHub stars: 3
Circulate Firing Snn Direct TrainingADirect training algorithm for SNNs with circulate-firing neurons and learnable surrogate gradients. Three core innovations for membrane potential dynamics optimization.researchgoperformanceVotes: 0GitHub stars: 3
Braininspired Graph Spiking Neural Networks For Commonsense Knowledge Representation And ReasoningA**arXiv ID:** 2207.05561 **Authors:** Hongjian Fang, Yi Zeng, Jianbo Tang, Yuwei Wang, Yao Liang, Xin Liu **Published:** 2022-07-11T05:22:38Z **Abstract:** How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performan...researchgoperformanceVotes: 0GitHub stars: 3
Brain Scale Snn SimulationABrain-scale spiking neural network simulation using network topology exploitation. Enables efficient large-scale SNN simulations by leveraging network structure. Activation: brain-scale, snn simulation, network topology, spiking neural networks, large-scale neural simulation.researchgoperformanceVotes: 0GitHub stars: 3
Brain NeuromorphicANeuromorphic VLSI systems take inspiration from biology to enable efficient emulation of large-scale spiking neural networks and to explore new computational paradigms. To establis... Activation: spiking neural network, neural dynamics, neuromorphicresearchpythongoVotes: 0GitHub stars: 3
Bayesian Speech SnnABayesian inference methodology for Spiking Neural Networks in speech processing. Uses IVON (Improved Variational Online Newton) to smooth the angular predictive landscape caused by threshold-based spike generation. Demonstrates improved NLL and Brier scores on Heidelberg Digits and Speech Commands datasets. Activation: bayesian SNN, speech SNN, surrogate gradient, IVON, loss landscape smoothing, uncertainty quantification, variational inference.researchpythonangularVotes: 0GitHub stars: 3
Axonal Delay As A Shortterm Memory For Feed Forward Deep Spiking Neural NetworksA**arXiv ID:** 2205.02115 **Authors:** Pengfei Sun, Longwei Zhu, Dick Botteldooren **Published:** 2022-04-20T16:56:42Z **Abstract:** The information of spiking neural networks (SNNs) are propagated between the adjacent biological neuron by spikes, which provides a computing paradigm with the promise of simulating the human brain. Recent studies have found that the time delay of neurons plays an important role in the learning process. Therefore, configuring the precise timing of the spike is a ...researchgitperformanceVotes: 0GitHub stars: 3
Arxiv 2608 20271 Catching The Rug Early Prediction Of Fraudulent MeACatching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning (arXiv: 2608.20271)researchgoapiVotes: 0GitHub stars: 3
Arxiv 2608 20220 Insufficiencybench Evaluating Llm Legal Advice OnAInsufficiencyBench: Evaluating LLM legal advice on underspecified user queries (arXiv: 2608.20220)researchgoVotes: 0GitHub stars: 3
Arxiv 2608 20054 What You Can T See Is What You Learn Restricted EvAWhat You Can't See Is What You Learn: Restricted Evidence Visibility Favors Compositional Generalization in Shared-Genome Language-Model Societies (arXiv: 2608.20054)researchgoperformanceVotes: 0GitHub stars: 3
Arxiv 2608 19888 Evidence Before Expansion Reuse Spawn Or Defer InAEvidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools (arXiv: 2608.19888)researchgoVotes: 0GitHub stars: 3
Arxiv 2608 19885 Separating Covariate Shift From Mechanism Change WASeparating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition (arXiv: 2608.19885)researchgoVotes: 0GitHub stars: 3
A Low Precision Simd Spiking Neural Compute EngineASpiking Neural Networks (SNNs) offer a promising solution for energy-efficient edge intelligence; however, their hardware deployment is constrained by memory overhead, inefficient scaling operations, ...researchgoVotes: 0GitHub stars: 3
Spiking Neural Networks Elephant ReinforcementASpiking Neural Networks with Elephant Reinforcement — finite stochastic spiking-neuron network where past firing activity modifies future excitability through reinforcement-dependent threshold.researchVotes: 0GitHub stars: 3
Sparsity Ceiling Spiking Networks EnergyAThe Sparsity Ceiling framework for analyzing where Spiking Neural Networks can and cannot trade activity for energy efficiency. Use when studying energy-efficiency limits in SNNs, analyzing the relationship between sparsity and computational capability, or evaluating neuromorphic hardware performance across different network architectures.researchgoperformanceVotes: 0GitHub stars: 3
Sparsity Ceiling Snn Energy EfficiencyAThe Sparsity Ceiling framework for analyzing where Spiking Neural Networks can and cannot trade activity for energy efficiency. Provides information-theoretic bounds on firing rates based on memory load, state width, and task difficulty. Use when analyzing SNN energy efficiency, neuromorphic hardware deployment, or comparing recurrent vs attention-based architectures.researchgogitVotes: 0GitHub stars: 3
Scaling Kubernetes To 2500 NodesASkill for AI agent capabilitiesresearchpythongoVotes: 0GitHub stars: 3