arXiv (OAI Expanded)
open access
A Memristive Synapse for Online STDP Learning and Inference in SNNs
This work presents a fully analog memristive synaptic circuit for online spike-timing-dependent plasticity (STDP) learning in spiking neural networks (SNNs). The proposed synapse integrates a local STDP circuit generating gradual timing-dependent conductance updates directly from pre- and post-synap
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
- Theory of non-linear spike-time-dependent plasticity#401911
- Neural Learning as a Game Induced by Spike-Timing-Dependent Plasticity#1035100
- Synaptic Plasticity as a Function of the Temporal Derivative#343619
- Improving Learning in Spiking Neural Networks#374444
- PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks#1061767
Record · ID 1109365
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