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connectome-wiring-statistical-dynamics-separation Separating wiring-specific from statistical control of dynamics in a complete connectome. Analysis of larval Drosophila brain showing coarse statistics set dynamical regime while specific wiring determines activity routing.
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name connectome-wiring-statistical-dynamics-separation description Separating wiring-specific from statistical control of dynamics in a complete connectome. Analysis of larval Drosophila brain showing coarse statistics set dynamical regime while specific wiring determines activity routing. version 1.0.0 category neuroscience arxiv_id 2606.17745 author Stavros Therianos institution Independent Researcher published 2026-06-16T00:00:00.000Z activation_words ["connectome dynamics","wiring statistics","statistical versus specific wiring","larval drosophila","connectome control","network operator gain","mushroom body dynamics","olfactory pathway routing","degree-weight matching","null model hierarchy"] related_skills ["connectome-wiring-statistics-control-dynamics","brain-network-controllability","connectome-constrained-neural-network","connectome-genetic-environmental-architecture","effective-plasticity"]
Separating Wiring-Specific from Statistical Control of Dynamics in a Complete Connectome
Core Innovation
首次在完整连接组 （果蝇幼虫脑）上系统性分离：
粗粒统计特性 ：决定全局动力学 regime（增益、维度、线性度）精细接线模式 ：决定活动传播路径和主导电路
核心发现 : 统计特性设定 regime，精细接线设定几何。
Methodology
Frozen Operator Assay
将连接组作为固定动力学算子 运行：
无单神经元参数调优 : 所有属性归因于接线Rate-based model : 无动作电位、时间常数、突触动力学Spectral radius ρ = 0.99 : Leaky-tanh update
W = connectome_weight_matrix
x_{t+1 } = tanh(ρ * W @ x_t + input )
Null Model Ladder
从最不保留接线到最保留接线：
Level Model Preserves 1 Unstructured Gaussian Nothing 2 Degree + Weight Matched In/out-degree, weight distribution 3 Block-Preserving Rewire + Cell-class architecture 4 Connectome Exact placement
关键 : Level 2-3 保留统计特性但 scramble 接线位置。
Structural-Dynamical Properties
Operator Gain : How much it amplifies inputDimensionality : How many directions surviveMode Leverage : Which neurons shape modesSparse-Input Routing : Where input lands
Key Results
Global Regime: Statistical Control
Degree+Weight matched ensemble reproduces :
Operator gain
Dimensionality
Near-linearity
Implication : 这些全局性质不依赖具体接线，只依赖统计特性。
Pathways: Wiring-Specific Control
Sparse input routing :
Connectome: Activity confined to olfactory pathway
Rewired networks: Activity floods widely
Leading modes concentrated in MB (learning center)
Rewiring distributes modes uniformly
Convergence neurons depleted from driving modes
Cell-Class Architecture Contribution Partial reproduction of confinement :
Block-preserving rewire captures ~50% of routing specificity
Remaining 50% driven by fine synaptic placement
MB-specific wiring concentration :
Not just cell-class labels
Fine-grained synapse placement matters
Null Hierarchy Working: Retraction Example Lateral horn localization claim :
Initial observation: LH concentrates driven-side modes
Size-matched control: Random sets show similar
Result : Claim retracted (false positive)
Anatomical Context
Larval Drosophila Brain
3,013 neurons total111,243 directed connections Strongly connected core : 2,825 neurons, 109,438 synapses536 self-loops
Identified Circuits Circuit Neurons Function Mushroom Body (MB) 231 Learning center Lateral Horn (LH) 201 Olfactory processing Central Complex (CX) 77 Navigation Remaining 2,316 Mixed populations
Input/Output Ports
Afferent : 80 input portsEfferent : 97 output ports
Theoretical Framework
Operator Gain
Dimensionality dim = count(eigenvalues with |λ| > threshold)
Mode Leverage (Adjoint-Side)
mode_leverage = W^T @ dominant_eigenvectors
Sparse-Input Routing
activation_pattern = W^n @ sparse_input
Biological Interpretation
Statistics → Regime Coarse features determine :
Overall responsiveness (gain)
Information capacity (dimensionality)
Linear/nonlinear behavior
Why : All neurons have similar connectivity statistics → similar dynamical role
Wiring → Geometry Specific placement determines :
Which pathways activate
Which circuits dominate dynamics
Where information flows
Why : Exact synapse locations create privileged channels
Mushroom Body Significance
Concentrates leading adjoint modes
Shapes which neurons drive dynamics
Learning center has outsized control
Not cell-class alone : Fine MB-specific wiring critical
Null Model Details
Degree + Weight Matching
for neuron in connectome:
in_degree[n] = count(inputs)
out_degree[n] = count(outputs)
total_weight[n] = sum (connections)
rewire_connections(preserve=in /out_degree, weights)
Block-Preserving Rewire
blocks = [MB, LH, CX, Others]
for block in blocks:
preserve_internal_connections(block)
scramble_between_blocks()
Size-Matched Random Control
circuit = MB neurons
random_set = random_neurons(size=231 )
compare(circuit, random_set)
Experimental Validation
White-Noise Drive
Input through afferent ports
Measure core state evolution
No behavioral/physiological state
Readout Metrics
Operator gain : Signal amplificationDimensionality : Active subspaceMode leverage : Driving neuronsRouting confinement : Pathway specificity
Limitations
What Model Omits
Action potentials
Single-neuron time constants
Synaptic/receptor kinetics
Neuromodulation
Behavioral state
Gap junctions
Interpretation Caution Not physiological simulation :
Structural-dynamical instrument
No odor-evoked activity claims
Wiring-only analysis
Applications
Connectome Analysis
Wiring vs statistics claims : Null model hierarchyCircuit dominance : Mode leverage analysisPathway routing : Sparse-input confinementArchitecture effects : Block-preserving tests
Comparative Connectomics
Cross-species statistics comparison
Developmental wiring changes
Evolutionary wiring optimization
Neural Network Theory
Weight initialization insights
Architectural inductive bias
Connectivity regime analysis
Technical Requirements
Dependencies
NumPy/SciPy (linear algebra)
NetworkX (graph operations)
Matplotlib (visualization)
Hardware
CPU: Network analysis
RAM: ~10GB for full connectome
Key Takeaways
Separation : Statistics set regime, wiring sets geometryNull hierarchy : Different claims need different controlsMB dominance : Learning center shapes dynamicsRetraction example : Null models catch false positivesFrozen operator : Wiring-only attribution
Citation @article{therianos2026connectome,
title={Separating wiring-specific from statistical control of dynamics in a complete connectome},
author={Therianos, Stavros},
journal={arXiv preprint arXiv:2606.17745},
year={2026}
}
Activation : Use when analyzing connectome dynamics, wiring vs statistics separation, network regime analysis, or mushroom body functional dominance in complete connectomes.
