Structural Properties of the Caenorhabditis elegans Neuronal Network

Abstract

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.

References in corpus (8)

• Power-law distributions in empirical data
• Biological network comparison using graphlet degree distribution
• Clustering in Complex Directed Networks
• Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
• Synchronization is optimal in non-diagonalizable networks
• Mesoscopic organization reveals the constraints governing C. elegans nervous system
• Impact of hierarchical modular structure on ranking of individual nodes in directed networks
• Link and subgraph likelihoods in random undirected networks with fixed and partially fixed degree sequence

Cited by in corpus (130)

• Multilayer Networks
• Powerlaw: a Python package for analysis of heavy-tailed distributions
• Griffiths phases and the stretching of criticality in brain networks
• What are higher-order networks?
• Explosive higher-order Kuramoto dynamics on simplicial complexes
• The multilayer connectome of Caenorhabditis elegans
• Chimera states in a multilayer network of coupled and uncoupled neurons
• Navigation of brain networks
• Chimera-like states in modular neural networks
• Synchronization of hypernetworks of coupled dynamical systems
• Topological analysis of the connectome of digital reconstructions of neural microcircuits
• Predicting missing links and their weights via reliable-route-based method
• Higher-order simplicial synchronization of coupled topological signals
• Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs
• Higher-order clustering in networks
• Phase transition in the economically modeled growth of a cellular nervous system
• Autonomous inference of complex network dynamics from incomplete and noisy data
• Exploring Randomly Wired Neural Networks for Image Recognition
• Spatio-temporal networks: reachability, centrality and robustness
• Diversity of information pathways drives scaling and sparsity in real-world networks
• Functional importance of noise in neuronal information processing
• Mechanical Intelligence Simplifies Control in Terrestrial Limbless Locomotion
• Flow-based network analysis of the Caenorhabditis elegans connectome
• Navigable maps of structural brain networks across species
• Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution
• Hyperedge overlap drives explosive collective behaviors in systems with higher-order interactions
• Low-dimensional functionality of complex network dynamics: Neuro-sensory integration in the Caenorhabditis elegans connectome
• Mapping and discrimination of networks in the complexity-entropy plane
• Synaptic polarity of the interneuron circuit controlling C. elegans locomotion
• Estimating the impact of structural directionality: How reliable are undirected connectomes?
• Do Brain Networks Evolve by Maximizing their Information Flow Capacity?
• Neurons as hierarchies of quantum reference frames
• Symmetry group factorization reveals the structure-function relation in the neural connectome of Caenorhabditis elegans
• Information flow through a model of the C. elegans klinotaxis circuit
• Colored motifs reveal computational building blocks in the C. elegans brain
• Structurally Robust Control of Complex Networks
• Finding role communities in directed networks using Role-Based Similarity, Markov Stability and the Relaxed Minimum Spanning Tree
• Excitable elements controlled by noise and network structure
• Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network
• A nonparametric two-sample hypothesis testing problem for random dot product graphs
• Geometric framework to predict structure from function in neural networks
• Information content of colored motifs in complex networks
• A new measure for community structures through indirect social connections
• Chaotic heteroclinic networks as models of switching behavior in biological systems
• ElegansNet: a brief scientific report and initial experiments
• Hub-activated signal transmission in complex networks
• Semiparametric spectral modeling of the Drosophila connectome
• Developmental time windows for axon growth influence neuronal network topology
• STDP-driven networks and the \emph{C. elegans} neuronal network
• Developmental trajectory of Caenorhabditis elegans nervous system governs its structural organization
• Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
• Deriving pairwise transfer entropy from network structure and motifs
• Modular structure in C. elegans neural network and its response to external localized stimuli
• Fast Approximate Quadratic Programming for Large (Brain) Graph Matching
• Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
• Percolation and the effective structure of complex networks
• Neuronal functional connectivity among multiple areas of the rat somatosensory system during spontaneous and evoked activities
• Cycle and flow trusses in directed networks
• Network properties of healthy and Alzheimer's brains
• Protein Structured Reservoir computing for Spike-based Pattern Recognition
• Self-Organized Stationary Patterns in Networks of Bistable Chemical Reactions
• Geometry, Topology and Simplicial Synchronization
• Dynamical Complexity in the C.elegans Neural Network
• Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study
• Dynamic Range in the C.elegans Brain Network
• Sizing the length of complex networks
• Robust Vertex Classification
• Revealing directed effective connectivity of cortical neuronal networks from measurements
• Nonequilibrium physics of brain dynamics
• Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
• Networks of noisy oscillators with correlated degree and frequency dispersion
• Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
• Stationary patterns in star networks of bistable units: Theory and application to chemical reactions
• Persistence of chimera states and the challenge for synchronization in real-world networks
• Inference of topology and the nature of synapses, and the flow of information in neuronal networks
• Null Models and Community Detection in Multi-Layer Networks
• Visualization in Connectomics
• Controlling network ensembles
• Complexes of Tournaments, Directionality Filtrations and Persistent Homology
• Hochschild homology, and a persistent approach via connectivity digraphs
• Critical excitation-inhibition balance in dense neural networks
• Multilayer adaptive networks in neuronal processing
• Synergistic Graph Fusion via Encoder Embedding
• Discovering universal statistical laws of complex networks
• The relationship between structure and function in locally observed complex networks
• Symmetry-driven network reconstruction through pseudobalanced coloring optimization
• A Latent Parameter Node-Centric Model for Spatial Networks
• Communities in C.elegans connectome through the prism of non-backtracking walks
• Clustering matrices through optimal permutations
• Info-Clustering: A Mathematical Theory for Data Clustering
• Community detection for directed networks revisited using bimodularity
• Computing persistent homology of directed flag complexes
• Deciphering neural circuits for Caenorhabditis elegans behavior by computations and perturbations to genome and connectome
• Automatic discovery of cell types and microcircuitry from neural connectomics
• The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match
• Predicting Dynamics on Networks Hardly Depends on the Topology
• Encoder Embedding for General Graph and Node Classification
• Network Dependence Testing via Diffusion Maps and Distance-Based Correlations
• Community Correlations and Testing Independence Between Binary Graphs
• From Neuronal Packets to Thoughtseeds: A Hierarchical Model of Embodied Cognition in the Global Workspace
• Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP
• Google matrix analysis of C.elegans neural network
• Global Practical Node and Edge Synchronization in Kuramoto Networks: A Submodular Optimization Framework
• Computing Homotopy Types of Directed Flag Complexes
• Simplex Closing Probabilities in Directed Graphs
• Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
• Potential role of a ventral nerve cord central pattern generator in forward and backward locomotion in Caenorhabditis elegans
• Theory of Interface: Category Theory, Directed Networks and Evolution of Biological Networks
• Fast and Scalable Multi-Kernel Encoder Classifier
• Synthetic ablations in the C. elegans nervous system
• Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
• Analysing Motifs in Multilayer Networks
• The growing topology of the C. elegans connectome
• Brain functions emerge as thermal equilibrium states of the connectome
• Statistical inference on errorfully observed graphs
• On the Role of Triadic Substructures in Complex Networks
• The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
• A Geometric Chung Lu model and the Drosophila Medulla connectome
• Network induces burst synchronisation in cat brain
• Causal screening for dynamical systems
• How Complex is a Complex Network? Insights from Linear Systems Theory
• Learning dynamic representations of the functional connectome in neurobiological networks
• Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data
• Neuro-PC: Causal Functional Connectivity from Neural Dynamics
• Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
• The control structure of the nematode Caenorhabditis elegans: neuro-sensory integration and propioceptive feedback
• Sparse Representation Classification via Screening for Graphs
• Analysis of neuronal sequences using pairwise biases
• Synaptic Noise Facilitates the Emergence of Self-Organized Criticality in the Caenorhabditis elegans Neuronal Network
• The rise and fall of hubs in Self-Organized Critical learning networks