arXiv (OAI Expanded)
open access
Event-Driven Learning for Spiking Neural Networks
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven prope
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting#1129698
- On the second-order optimization for spiking neural networks#1077308
- Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling#1154519
- Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware#799641
- SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models#781142
Record · ID 1122884
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive