arXiv (All)
open access
A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrog
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
- Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling#1154519
- On the second-order optimization for spiking neural networks#1077308
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting#137543
Record · ID 998423
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