arXiv (All)
open access
Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling
Spiking neural networks (SNNs) offer low-energy sequence modeling through sparse, event-driven computation. However, interactions among spike encoding, neuronal dynamics, and information propagation complicate architecture design. Existing SNN sequence models often adapt artificial neural network (A
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
- Causal pieces: analysing and improving spiking neural networks piece by piece#1141412
- Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware#799641
- Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams#1109733
- Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams#919283
Record · ID 1154519
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