arXiv (All)
open access
Beyond Low-Pass Dynamics: Frequency-Selective Spiking Reservoirs with Resonant Neurons
Spiking reservoir computing combines the training efficiency of fixed recurrent reservoirs with the energy efficiency of event-driven inference, where computation is triggered by sparse spikes rather than executed densely at every timestep. However, existing spiking reservoirs almost exclusively rel
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
- The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks#1357403
- Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip#196451
- On the second-order optimization for spiking neural networks#1077308
- Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware#19786
- Contrastive Attention Mitigates Spectral Bias in Spiking Transformers#1156675
Record · ID 1488698
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