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- Transport Mean Field SnnTransport Mean Field methodology for approximate macroscopic dynamics of spiking neural networks. Derives population firing rate evolution from initial voltage distributions via transport (advection) solution to the Fokker-Planck system, unlike earlier mean field approaches based on asynchronous steady-state solutions. Assumes slow time-varying inputs and excitation-driven regime. Use when: analyzing SNN population dynamics, deriving mean field approximations, studying firing rate fluctuation...Votes: 0GitHub stars: 3
- Transport Mean Field Snn DynamicsTransport mean field methodology for approximating macroscopic dynamics of spiking neural networks. Analytically derives firing rate fluctuations in coupled integrate-and-fire populations via solutions to the transport equation and Fokker-Planck system.Votes: 0GitHub stars: 3
- Threshold Based Snn Event DrivenSNN threshold policies for event-driven IoT status updates.Votes: 0GitHub stars: 3
- Threshold Based Snn Event Driven StatusThreshold-Based Spiking Neural Networks for Event-Driven Status Update Systems - lightweight RL approach using SNNs with explicit threshold policy representation for IoT energy-efficient communication. Use when optimizing Age of Information (AoI) and transmission energy in event-driven IoT systems.Votes: 0GitHub stars: 3
- Threshold Based Snn Event Driven Status Update SystemsThreshold policies for event-driven IoT status updates.Votes: 0GitHub stars: 3
- Temporal Switch Neuromorphic TransferModel-free temporal-switch (TS) framework for transferable lightweight neuromorphic computing. Enables direct transfer of trained models to unseen hardware devices without post-training calibration by incorporating a broader spectrum of devices during training. Addresses device-to-device variations that undermine practical advantages of neuromorphic computing. Activation: temporal switch framework, neuromorphic transfer, device variation robustness, memristor reservoir computing, model-free t...Votes: 0GitHub stars: 3
- Spikingmoe Sdprompt SnnResearch skill for SDPrompt-guided dynamic expert fusion in spiking neural networks.Votes: 0GitHub stars: 3
- Spiking Transformer Effective Dimension TheorySpiking Transformers: Effective Dimension Theory — arXiv:2604.15769 (April 2026). Develops effective dimension theory for Spiking Transformers via Neural Tangent Kernel (NTK) framework, analyzing how spiking mechanisms (threshold, reset, refractory period) reduce model expressivity and enable compression. Covers firing rate statistics, membrane time constants, optimal hyperparameter derivation, and spiking NTK formulation.Votes: 0GitHub stars: 3
- Spiking Sleep Waves Memory ConsolidationSpiking neural network modeling of sleep-related brain waves (spindles, slow oscillations, theta-gamma coupling) for understanding memory consolidation mechanisms. Applies to brain simulation, sleep disorder modeling, neuromorphic memory systems, cognitive AI. 触发词: sleep brain waves, spindles, slow oscillations, theta-gamma coupling, spiking sleep model, memory consolidation SNN, thalamocortical, hippocampalVotes: 0GitHub stars: 3
- Spiking Neural Network AnalysisAnalyze Spiking Neural Network (SNN) papers, extract technical patterns from knowledge graph, and identify reusable research methodologies for neuromorphic computing.Votes: 0GitHub stars: 3
- Spiking Neural Architecture SearchNeural Architecture Search (NAS) methodology for Spiking Neural Networks (SNNs). Covers search spaces, strategies, evaluation methods, and performance optimization. Based on arXiv:2604.16889 (April 2026).Votes: 0GitHub stars: 3
- Spiking Generative Networks StpSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Spiking Connectome Hierarchical State SpaceThis work presents the Parallelized Hierarchical Connectome (PHC), a general framework that upgrades temporal-only State-Space Models (SSMs) into spatiotemporal recurrent networks.... Activation: spiking neural network, connectome, state-space modelVotes: 0GitHub stars: 3
- Spiking Brain Complex NetworksSpiking neural networks modeling on complex brain networks. Combines SNN dynamics with realistic brain connectivity to study neural dynamics and brain function. (arXiv:2604.07361, 2026-04)Votes: 0GitHub stars: 3
- Spiker Ll Fpga Snn AcceleratorSpiker-LL methodology — FPGA-based SNN accelerator enabling adaptive local learning at the edge. Extends the open-source Spiker+ inference architecture with efficient support for the Spiking Time Sparse Feedback (STSF) local learning rule. Achieves up to 93% accuracy (MNIST/F-MNIST/DIGITS), sub-millisecond latency, and <0.1 mJ per inference, remaining DSP-free and highly scalable for edge-FPGA deployments. Use when: designing edge SNN hardware accelerators, implementing on-device learning, bu...Votes: 0GitHub stars: 3
- Soliton Waves Wstdp SnnSoliton-like waves in 2D recurrent spiking neural networks with weighted STDP - biologically plausible discrete-time neuron model combining multiplicative STDP, divisive normalization, and homeostatic threshold adaptation to generate stable wave propagation.Votes: 0GitHub stars: 3
- Snn Topology SimulationTopology-exploiting optimization for brain-scale spiking neural network simulations — reducing communication bottlenecks via network-aware compute node assignment and dynamic load balancing.Votes: 0GitHub stars: 3
- Snn Simulation Tools ReviewSNN Simulation Tools ReviewVotes: 0GitHub stars: 3
- Snn Reconstruction AutapsesReconstructing spiking neural networks using a single neuron with autapses. Topology inference from single-neuron dynamics. Activation: snn reconstruction, autapses, network topology, single neuron, reverse engineering.Votes: 0GitHub stars: 3
- Snn Multimodal BrainBrain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval. Activation: braininspired, multimodal, spiking, imagetext, retrievalVotes: 0GitHub stars: 3
- Snn Learning Rules DynamicsSNN learning rules, dynamics, and learning ability analysis methodology. Covers Hebbian/anti-Hebbian learning, reward-based learning, backpropagation, surrogate gradients, and their relationships to network dynamics.Votes: 0GitHub stars: 3
- Snn Heterogeneous Synaptic DelaysWorking memory implementation in recurrent spiking neural networks using heterogeneous synaptic delays. Biological approach to memory with variable delay timescales. Activation: working memory, synaptic delays, recurrent snn, heterogeneous delays, neural memory.Votes: 0GitHub stars: 3
- Snn Elephant ReinforcementSpiking Neural Networks with Elephant Reinforcement — finite stochastic spiking-neuron network where past firing activity modifies future excitability through reinforcement-dependent threshold.Votes: 0GitHub stars: 3
- Snn Beyond Dales PrincipleCollective dynamics in spiking neural networks beyond Dale's principle. Mixed excitatory-inhibitory neurons. Activation: dale's principle, mixed neurons, collective dynamics, excitatory inhibitory, snn dynamics.Votes: 0GitHub stars: 3