58 research outputs found
Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome
The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors
Modular structure in C. elegans neural network and its response to external localized stimuli
Synchronization plays a key role in information processing in neuronal
networks. Response of specific groups of neurons are triggered by external
stimuli, such as visual, tactile or olfactory inputs. Neurons, however, can be
divided into several categories, such as by physical location, functional role
or topological clustering properties. Here we study the response of the
electric junction C. elegans network to external stimuli using the partially
forced Kuramoto model and applying the force to specific groups of neurons.
Stimuli were applied to topological modules, obtained by the ModuLand
procedure, to a ganglion, specified by its anatomical localization, and to the
functional group composed of all sensory neurons. We found that topological
modules do not contain purely anatomical groups or functional classes,
corroborating previous results, and that stimulating different classes of
neurons lead to very different responses, measured in terms of synchronization
and phase velocity correlations. In all cases, however, the modular structure
hindered full synchronization, protecting the system from seizures. More
importantly, the responses to stimuli applied to topological and functional
modules showed pronounced patterns of correlation or anti-correlation with
other modules that were not observed when the stimulus was applied to ganglia.Comment: 23 pages, 6 figure
Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
Modeling of complex adaptive systems has revealed a still poorly understood beneïŹt of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor conïŹgurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneïŹcial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios
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Micro-connectomics: probing the organization of neuronal networks at the cellular scale.
Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with RamĂłn y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group
Micro-connectomics: probing the organization of neuronal networks at the cellular scale.
Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with RamĂłn y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group
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