ArticlePLoS computational biology2017
Optimal synaptic signaling connectome for locomotory behavior in Caenorhabditis elegans: Design minimizing energy cost.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 22 citations in OpenAlex.
- A data-driven biology-based network model reproduces C. elegans premotor neural dynamics.PLoS computational biology · 2025Article
- A Novel and Functionally Diverse Class of Acetylcholine-Gated Ion Channels.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Reprogramming the topology of the nociceptive circuit in C. elegans reshapes sexual behavior.Current biology : CB · 2022Article
- Computational Inference of Synaptic Polarities in Neuronal Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Fixed Point Attractor Theory Bridges Structure and Function inFrontiers in neuroscience · 2022Article
- Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network.PLoS computational biology · 2020Article
- Article
- Novel Technological Advances in Functional Connectomics inJournal of developmental biology · 2019Review
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The detailed knowledge of C. elegans connectome for 3 decades has not contributed dramatically to our understanding of worm's behavior. One of main reasons for this situation has been the lack of data on the type of synaptic signaling between particular neurons in the worm's connectome. The aim of this study was to determine synaptic polarities for each connection in a small pre-motor circuit controlling locomotion. Even in this compact network of just 7 neurons the space of all possible patterns of connection types (excitation vs. inhibition) is huge. To deal effectively with this combinatorial problem we devised a novel and relatively fast technique based on genetic algorithms and large-scale parallel computations, which we combined with detailed neurophysiological modeling of interneuron dynamics and compared the theory to the available behavioral data. As a result of these massive computations, we found that the optimal connectivity pattern that matches the best locomotory data is the one in which all interneuron connections are inhibitory, even those terminating on motor neurons. This finding is consistent with recent experimental data on cholinergic signaling in C. elegans, and it suggests that the system controlling locomotion is designed to save metabolic energy. Moreover, this result provides a solid basis for a more realistic modeling of neural control in these worms, and our novel powerful computational technique can in principle be applied (possibly with some modifications) to other small-scale functional circuits in C. elegans.
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