arXiv (All)
open access
"More Is Different'' in Neural Circuits: Algebraic Emergence of Effective Theories in Canonical Recurrent Motifs of Biological Neuronal Networks
biological-physicschaotic-dynamicsformal-languages-and-automata-theoryneural-and-evolutionary-computingneurons-and-cognition
Canonical neural circuit motifs are usually described functionally: divisive normalization rescales population activity by a pooled signal, and winner-take-all competition selects one pattern through recurrent excitation and shared inhibition. We represent them, and their compositions, algebraically
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
- "More Is Different'' in Neural Circuits: Algebraic Emergence of Effective Theories in Canonical Recurrent Motifs of Biological Neuronal Networks#798027
- A functional influence based circuit motif that constrains the set of plausible algorithms of cortical function#273155
- Hyperchaos and complex dynamical regimes in $N$-dimensional neuron lattices#997435
- Layered Dissipative Neural Fields: Bounding the Dimension of the Population Activity Manifold with Infinitely Many Neurons#1012939
- Elementary Dynamics of Neural Microcircuits#321432
Record · ID 799450
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