HAL (France)
open access
A theoretical multiscale examination of visual spatial attention in the mouse cerebral cortex
Computational approaches modeling neurons and their networks are essential for untangling the complex interactions underlying brain functions such as visual attention. This thesis aims to construct large-scale biophysically mimetic spiking neural network models of the mouse cortex to examine how vis
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
- Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing#19788
- Visual cortical dynamics supporting predictable attentional capture.#305063
- Functional bipartite invariance in mouse primary visual cortex receptive fields#5616
- A computational framework for cortical learning#401864
- Receptive fields, binocular interaction and functional architecture in the cat's visual cortex#6453
Record · ID 1053571
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