| name | topology-dependent-polychronous-neuronal-groups |
| description | Topology-Dependent Emergence of Polychronous Neuronal Groups using Recurrence-Plot characterization. Small-world topology as structural optimum for polychronization with label-free PNG identification via sparse-dot-product Recurrence Plot framework. |
| version | 1 |
| author | extracted from arXiv 2606.25874v1 (Carneiro, Jiofack, Ferreira) |
| date_created | 2026-06-25T00:00:00.000Z |
| source | arXiv 2606.25874v1 |
| categories | ["neuroscience","spiking-neural-networks","computational-neuroscience","neural-dynamics","brain-network"] |
| tags | ["polychronous-neuronal-groups","PNG","recurrence-plot","STDP","axonal-delays","Izhikevich-neurons","watts-strogatz","small-world-topology","network-structure"] |
| activation_keywords | ["polychronous neuronal groups","PNG emergence","recurrence plot neural networks","topology neural computation","STDP polychronization","axonal delays PNG","small-world neural network","Izhikevich PNG","clustering coefficient neural","recurrence quantification analysis neuroscience"] |
| related_skills | ["spiking-neural-network-analysis","stdp-spiking-transformer-attention","spiking-oscillation-mapping","brain-inspired-snn-pattern-analysis"] |
Topology-Dependent Emergence of Polychronous Neuronal Groups: A Recurrence-Plot Characterization
Overview
Core Discovery: Small-world topology is the structural optimum for polychronization in neural networks. The clustering coefficient C is the primary structural driver of Polychronous Neuronal Group (PNG) yield, with transition from ring-lattice to random graph reducing representational capacity by >90%.
Methodological Innovation: Sparse-dot-product Recurrence Plot (RP) framework for label-free PNG identification — identifies PNGs as unit-slope diagonal structures in phase-space recurrence matrix, independent of anatomical neuron labeling.
Key Findings
1. PNG Structural Determinants
- N=1000 Izhikevich neurons simulated over 10 hours biological time
- 1545 unique PNGs identified via offline event-driven detection
- Clustering coefficient C drives PNG yield: C~0.35 (ring-lattice) →
850 PNGs; C0.20 (random) → <50 PNGs
- >90% representational capacity reduction from structured to random topology
2. Small-World Topology as Optimum
- Ring-lattice (high clustering) supports rich PNG repertoire
- Random graphs (low clustering) drastically reduce PNG diversity
- Small-world networks optimize trade-off between local structure and global connectivity
3. Recurrence Plot PNG Decoder
- Sparse-dot-product RP identifies PNGs without neuron labeling
- PNGs appear as unit-slope diagonal structures in phase-space recurrence matrix
- Recurrence Quantification Analysis: DET~0.65 quantifies trajectory reproducibility
- Label-free, principled approach to PNG identification
Technical Implementation
Izhikevich Network Simulation
