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Separating wiring-specific from statistical control of dynamics in complete connectomes - clarifying which connectome-based claims rest on wiring alone
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基于 SOC 职业分类
| name | connectome-wiring-statistics-control-dynamics |
| description | Separating wiring-specific from statistical control of dynamics in complete connectomes - clarifying which connectome-based claims rest on wiring alone |
| category | computational-neuroscience |
| version | 1 |
| created | 2026-06-18T00:00:00.000Z |
| arxiv_id | 2606.17745v1 |
| authors | ["Stavros Therianos"] |
| keywords | ["connectome","wiring diagram","dynamical regime","statistical control","brain dynamics","Drosophila","rate-based model","recurrent dynamics","mushroom body","olfactory pathway"] |
| activation | connectome, wiring diagram, neural dynamics, statistical control, rate-based operator, complete synaptic reconstruction |
This methodology addresses a fundamental question in connectomics: How far does a wiring diagram alone fix a circuit's activity versus finer physiological details it doesn't record? The approach uses complete synaptic wiring diagrams as fixed, rate-based dynamical operators without fitted single-neuron parameters, comparing them against a hierarchy of randomized networks preserving coarser wiring statistics.
Statistical Control (Regime)
Wiring-Specific Control (Geometry)
Fixed Parameters
Dynamical Regime Analysis
# Example: Drosophila larval connectome
# Requires: Electron microscopy reconstruction data
# Input: Complete synaptic wiring diagram with:
# - All neuron positions
# - All synaptic connections
# - Connection strengths (synaptic counts)
# Rate-based dynamical model
import numpy as np
class ConnectomeOperator:
def __init__(self, wiring_matrix, connection_strengths):
self.W = wiring_matrix # NxN connectivity matrix
self.S = connection_strengths # Synaptic counts
self.N = len(wiring_matrix) # Number of neurons
def compute_operator(self):
# Construct dynamical operator A
# A = W * S (weighted by connection strengths)
self.A = self.W * self.S
return self.A
def generate_statistical_control(connectome):
# Preserve coarse statistics
# Randomize precise wiring pattern
stats = {
'mean_degree': np.mean(np.sum(connectome.W != 0, axis=1)),
'degree_distribution': np.histogram(np.sum(connectome.W != 0, axis=1)),
'connection_strength_dist': np.histogram(connectome.S),
}
# Generate random network with same statistics
random_W = generate_random_network(stats)
return random_W
def compare_dynamics(operator_A, operator_B):
# Eigenvalue spectra comparison
eigenvalues_A = np.linalg.eigvals(operator_A)
eigenvalues_B = np.linalg.eigvals(operator_B)
kl_div = compute_kl_divergence(eigenvalues_A, eigenvalues_B)
frobenius = np.linalg.norm(operator_A - operator_B)
wasserstein = compute_wasserstein(operator_A, operator_B)
return {
'KL_divergence': kl_div,
'Frobenius_norm': frobenius,
'Wasserstein_distance': wasserstein
}
def analyze_activity_geometry(operator, input_pattern):
# Where does activity travel?
# Which circuits shape it?
# Forward dynamics: activity propagation
activity_trajectory = simulate_dynamics(operator, input_pattern)
# Adjoint modes: which neurons shape dynamics
adjoint_modes = compute_adjoint(operator)
return {
'activity_trajectory': activity_trajectory,
'adjoint_modes': adjoint_modes,
'pathway_confinement': compute_confinement(activity_trajectory)
}
Distinguish Wiring Claims
Circuit Mechanism Discovery
Learning Center Analysis
Statistical Sufficiency
Geometry Optimization
Complete Connectome
Physiological Measurements
Behavioral Correlates
Compare Full vs. Statistical Models
Pathway Confinement Tests
Adjoint Mode Validation
Olfactory Pathway
Mushroom Body
Whole-Brain Dynamics
Coarse Statistics → Regime
Precise Wiring → Geometry
Separation Validity
connectome-constrained-neural-network - Neural networks with connectome constraintsneural-dynamics-analysis - Neural dynamics analysis methodsbrain-network-controllability - Network control theoryconnectome-wiring-statistics - Wiring statistics analysisParameter Fitting
Regime Mismatch
Geometry Misinterpretation
Incomplete Connectomes
✅ Complete synaptic wiring diagram
✅ Fixed rate-based operator (no fitted parameters)
✅ Hierarchy of randomized statistical controls
✅ Multiple comparison metrics (KL, Frobenius, Wasserstein)
✅ Activity geometry analysis
✅ Adjoint mode computation
✅ Pathway confinement measures
❌ Incomplete connectivity data
❌ Tuned single-neuron physiology
❌ Single comparison metric
❌ Missing statistical controls
❌ No geometry analysis