arXiv (All)
open access
Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neura
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
- Activity-Dependent Plasticity in Morphogenetically-Grown Recurrent Networks#1422677
- SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting#1129698
- FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT#1341806
- Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks#1412555
- Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks#1394245
Record · ID 1458313
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