scholarly journals STDP-driven networks and the C. elegans neuronal network

2010 ◽  
Vol 389 (18) ◽  
pp. 3900-3914 ◽  
Author(s):  
Quansheng Ren ◽  
Kiran M. Kolwankar ◽  
Areejit Samal ◽  
Jürgen Jost
Keyword(s):  
Neuronal Network ◽  
C Elegans

scholarly journals Soft-wired long-term memory in a natural recurrent neuronal network

2020 ◽  
Author(s):  
Miguel A. Casal ◽  
Santiago Galella ◽  
Oscar Vilarroya ◽  
Jordi Garcia-Ojalvo
Keyword(s):  
Neuronal Network ◽  
Neuronal Networks ◽  
Initial Conditions ◽  
Permutation Entropy ◽  
Long Term Memory ◽  
Term Memory ◽  
C Elegans ◽  
Living Organisms ◽  
Recurrent Connections

Neuronal networks provide living organisms with the ability to process information. They are also characterized by abundant recurrent connections, which give rise to strong feed-back that dictates their dynamics and endows them with fading (short-term) memory. The role of recurrence in long-term memory, on the other hand, is still unclear. Here we use the neuronal network of the roundworm C. elegans to show that recurrent architectures in living organisms can exhibit long-term memory without relying on specific hard-wired modules. A genetic algorithm reveals that the experimentally observed dynamics of the worm’s neuronal network exhibits maximal complexity (as measured by permutation entropy). In that complex regime, the response of the system to repeated presentations of a time-varying stimulus reveals a consistent behavior that can be interpreted as soft-wired long-term memory.A common manifestation of our ability to remember the past is the consistence of our responses to repeated presentations of stimuli across time. Complex chaotic dynamics is known to produce such reliable responses in spite of its characteristic sensitive dependence on initial conditions. In neuronal networks, complex behavior is known to result from a combination of (i) recurrent connections and (ii) a balance between excitation and inhibition. Here we show that those features concur in the neuronal network of a living organism, namely C. elegans. This enables long-term memory to arise in an on-line manner, without having to be hard-wired in the brain.

scholarly journals Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

2018 ◽  
Vol 373 (1758) ◽  
pp. 20170377 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Caenorhabditis Elegans ◽  
Graphical Models ◽  
Neuronal Network ◽  
Graphical Model ◽  
Time Series Data ◽  
Series Data ◽  
Neuronal Responses ◽  
C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

scholarly journals Computing Cliques and Cavities in Networks

2021 ◽  
Author(s):  
Dinghua Shi ◽  
Zhifeng Chen ◽  
Xiang Sun ◽  
Qinghua Chen ◽  
Chuang Ma ◽  
...  
Keyword(s):  
Neuronal Network ◽  
Search Algorithm ◽  
Maximum Clique ◽  
Characteristic Number ◽  
Large Network ◽  
C Elegans ◽  
Brain Functions ◽  
Complete Problem ◽  
Np Complete ◽  
And Function

Abstract Complex networks have complete subgraphs such as nodes, edges, triangles, etc., referred to as cliques of different orders. Notably, cavities consisting of higher-order cliques have been found playing an important role in brain functions. Since searching for the maximum clique in a large network is an NP-complete problem, we propose using k-core decomposition to determine the computability of a given network subject to limited computing resources. For a computable network, we design a search algorithm for finding cliques of different orders, which also provides the Euler characteristic number. Then, we compute the Betti number by using the ranks of the boundary matrices of adjacent cliques. Furthermore, we design an optimized algorithm for finding cavities of different orders. Finally, we apply the algorithm to the neuronal network of C. elegans in one dataset, and find all of its cliques and some cavities of different orders therein, providing a basis for further mathematical analysis and computation of the structure and function of the C. elegans neuronal network.

scholarly journals A new computational model captures fundamental architectural features of diverse biological networks

2016 ◽  
Author(s):  
Bader Al-Anzi ◽  
Noah Olsman ◽  
Christopher Ormerod ◽  
Sherif Gerges ◽  
Georgios Piliouras ◽  
...  
Keyword(s):  
Neuronal Network ◽  
Biological Networks ◽  
Regulatory Protein ◽  
Structural Features ◽  
Path Selection ◽  
Selection Mechanism ◽  
Graph Models ◽  
C Elegans ◽  
Architectural Features ◽  
Complete Networks

ABSTRACTComplex biological systems are often represented by network graphs; however, their structural features are not adequately captured by existing computational graph models, perhaps because the datasets used to assemble them are incomplete and contain elements that lack shared functions. Here, we analyze three large, near-complete networks that produce specific cellular or behavioral outputs: a molecular yeast mitochondrial regulatory protein network, and two anatomical networks of very different scale, the mouse brain mesoscale connectivity network, and the C. elegans neuronal network. Surprisingly, these networks share similar characteristics. All consist of large communities composed of modules with general functions, and topologically distinct subnetworks spanning modular boundaries responsible for their more specific phenotypical outputs. We created a new model, SBM-PS, which generates networks by combining communities, followed by adjustment of connections by a ‘path selection’ mechanism. This model captures fundamental architectural features that are common to the three networks.

The glycomes of Caenorhabditis elegans and other model organisms

2002 ◽  
Vol 69 ◽  
pp. 117-134 ◽  
Author(s):  
Stuart M. Haslam ◽  
David Gems ◽  
Howard R. Morris ◽  
Anne Dell
Keyword(s):  
Caenorhabditis Elegans ◽  
Current Knowledge ◽  
Structural Data ◽  
Model Organisms ◽  
Entire Genome ◽  
C Elegans ◽  
The Face ◽  
Area Of Concern ◽  
Nematode Worm

There is no doubt that the immense amount of information that is being generated by the initial sequencing and secondary interrogation of various genomes will change the face of glycobiological research. However, a major area of concern is that detailed structural knowledge of the ultimate products of genes that are identified as being involved in glycoconjugate biosynthesis is still limited. This is illustrated clearly by the nematode worm Caenorhabditis elegans, which was the first multicellular organism to have its entire genome sequenced. To date, only limited structural data on the glycosylated molecules of this organism have been reported. Our laboratory is addressing this problem by performing detailed MS structural characterization of the N-linked glycans of C. elegans; high-mannose structures dominate, with only minor amounts of complex-type structures. Novel, highly fucosylated truncated structures are also present which are difucosylated on the proximal N-acetylglucosamine of the chitobiose core as well as containing unusual Fucα1–2Gal1–2Man as peripheral structures. The implications of these results in terms of the identification of ligands for genomically predicted lectins and potential glycosyltransferases are discussed in this chapter. Current knowledge on the glycomes of other model organisms such as Dictyostelium discoideum, Saccharomyces cerevisiae and Drosophila melanogaster is also discussed briefly.

How do histone modifications contribute to transgenerational epigenetic inheritance in C. elegans?

2020 ◽  
Vol 48 (3) ◽  
pp. 1019-1034 ◽  
Author(s):  
Rachel M. Woodhouse ◽  
Alyson Ashe
Keyword(s):  
Histone Modifications ◽  
Molecular Mechanisms ◽  
Epigenetic Inheritance ◽  
Nematode Caenorhabditis Elegans ◽  
Histone Methyltransferases ◽  
Transgenerational Epigenetic Inheritance ◽  
C Elegans ◽  
Recent Developments ◽  
Gene Regulatory

Gene regulatory information can be inherited between generations in a phenomenon termed transgenerational epigenetic inheritance (TEI). While examples of TEI in many animals accumulate, the nematode Caenorhabditis elegans has proven particularly useful in investigating the underlying molecular mechanisms of this phenomenon. In C. elegans and other animals, the modification of histone proteins has emerged as a potential carrier and effector of transgenerational epigenetic information. In this review, we explore the contribution of histone modifications to TEI in C. elegans. We describe the role of repressive histone marks, histone methyltransferases, and associated chromatin factors in heritable gene silencing, and discuss recent developments and unanswered questions in how these factors integrate with other known TEI mechanisms. We also review the transgenerational effects of the manipulation of histone modifications on germline health and longevity.

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