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Methods/SNN

SNN

Spiking Neural Networks

Introduced 2000363 papers
Source Paper

Description

Spiking Neural Networks (SNNs) are a class of artificial neural networks inspired by the structure and functioning of the brain's neural networks. Unlike traditional artificial neural networks that operate based on continuous firing rates, SNNs simulate the behavior of individual neurons through discrete spikes or action potentials. These spikes are triggered when the neuron's membrane potential reaches a certain threshold, and they propagate through the network, communicating information and triggering subsequent neuron activations. This spike-based communication allows SNNs to capture the temporal dynamics of information processing and exhibit asynchronous, event-driven behavior, making them well-suited for tasks such as temporal pattern recognition, event detection, and real-time processing. SNNs have gained attention due to their potential in efficiently processing and encoding information, offering advantages in energy efficiency, robustness, and compatibility with neuromorphic hardware architectures.

Papers Using This Method

Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation2025-09-04Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing2025-06-25NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning2025-06-17Probabilistic Modeling of Spiking Neural Networks with Contract-Based Verification2025-06-16FeNN: A RISC-V vector processor for Spiking Neural Network acceleration2025-06-13Integration of Contrastive Predictive Coding and Spiking Neural Networks2025-06-10Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules2025-06-07SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems2025-06-03Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control2025-05-30Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing2025-05-28Energy-Efficient Deep Reinforcement Learning with Spiking Transformers2025-05-20SPKLIP: Aligning Spike Video Streams with Natural Language2025-05-19Spiking Neural Networks with Random Network Architecture2025-05-19SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks2025-05-18Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning2025-05-18Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware2025-05-18Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection2025-05-16Energy efficiency analysis of Spiking Neural Networks for space applications2025-05-16Phi: Leveraging Pattern-based Hierarchical Sparsity for High-Efficiency Spiking Neural Networks2025-05-16SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity2025-05-15