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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.
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基于 SOC 职业分类
| 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"] |
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.
# Izhikevich neuron model parameters
# N = 1000 neurons
# STDP plasticity with heterogeneous axonal delays
# Watts-Strogatz topology sweep: p ∈ [0, 1]
# Clustering coefficient C as structural metric
# PNG detection algorithm (event-driven, offline)
# 1. Track spike timings across all neurons
# 2. Identify time-locked firing cascades
# 3. Verify reproducibility via STDP stabilization
# Phase-space reconstruction from spike trains
# Sparse dot-product for recurrence matrix
# Diagonal structures = PNG signatures
# Recurrence Quantification Analysis (RQA)
# - DET (determinism): PNG reproducibility
# - LAM (laminarity): cascade stability
# - TT (trapping time): PNG duration
# Watts-Strogatz rewiring probability sweep
for p in [0.0, 0.05, 0.1, 0.15, 0.2, ..., 1.0]:
network = watts_strogatz(N=1000, k=10, p=p)
C = clustering_coefficient(network)
PNGs = detect_polychronous_groups(network, duration=10h)
yield[p] = len(PNGs)
Carneiro, L. A. T. X., Jiofack, A. D., & Ferreira, F. F. (2026). Topology-Dependent Emergence of Polychronous Neuronal Groups: A Recurrence-Plot Characterization. arXiv:2606.25874v1.