| name | synaptic-motifs-mean-field-dynamics |
| description | Mean-field theory linking microscale synaptic motifs to macroscopic heterogeneous population dynamics in neural networks. Use when studying synaptic-resolution connectomics, second-order motifs, random RNNs with cell types, or heterogeneous population dynamics. |
| tags | ["computational-neuroscience","mean-field-theory","synaptic-motifs","connectomics","RNN"] |
Synaptic Motifs Bridge Microscale Structure and Macroscale Nonlinear Dynamics
arXiv: 2606.27946v1 (June 26, 2026)
Authors: Meiyi Zhang, Jinjian Yu, Louis Tao, Yuxiu Shao
Categories: q-bio.NC, cond-mat.dis-nn, cs.NE
Core Contribution
Demonstrates that microscale synaptic structures (second-order motifs) contribute to macroscopic heterogeneous population dynamics in ways canonical brain circuit models cannot capture.
Key Methodology
1. Random RNN Framework with Cell Types
- Creates random RNNs with:
- Various cell types (P populations)
- Nonlinear non-negative neural responses
- Arbitrary marginal and second-order correlated synaptic statistics
2. Mean-Field Low-Rank Equations
- Derives mean-field equations for P-population networks
- Pre/post synaptic population identities determine synaptic and motif strengths
- Framework requires 2P latent dynamic variables:
- P variables: mean population activity
- P variables: within-population variability
3. Chain Motifs and Variability Integration
- Chain motifs induce correlations in synaptic variability
- Enable microscopic fluctuations to integrate and influence mesoscopic mean population dynamics
4. Application: Mouse V1 Reverse Engineering
- Applied to reverse engineer network connectivity
- Recapitulates heterogeneous activity across populations in mouse primary visual cortex
Key Findings
- Bridging scales: Microscale synaptic motifs (pairs of correlated synaptic couplings) directly shape macroscale heterogeneous population dynamics
- Chain motifs matter: Second-order chain motifs enable microscopic fluctuations to propagate to mesoscopic scales
- Testable predictions: Framework provides predictions about relationship between fine-scale connectivity, heterogeneous dynamics, and functional computations
Activation Keywords
synaptic motifs, second-order motifs, mean-field, heterogeneous dynamics, connectomics, population dynamics, random RNN, visual cortex, microscale-macroscale
Related Work
- Random matrix theory for neural networks
- Mean-field theory for balanced networks
- Synaptic-resolution connectomics (e.g., fly larva brain)
