{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T21:24:26Z","timestamp":1778793866642,"version":"3.51.4"},"reference-count":31,"publisher":"AIP Publishing","issue":"1","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:p>We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.<\/jats:p>","DOI":"10.1063\/1.4939837","type":"journal-article","created":{"date-parts":[[2016,1,13]],"date-time":"2016-01-13T23:58:32Z","timestamp":1452729512000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":12,"title":["Dynamic range in the <i>C. elegans<\/i> brain network"],"prefix":"10.1063","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7195-6892","authenticated-orcid":false,"given":"Chris G.","family":"Antonopoulos","sequence":"first","affiliation":[{"name":"University of Essex Department of Mathematical Sciences, , Wivenhoe Park, CO4 3SQ Colchester,","place":["United Kingdom"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2016,1,13]]},"reference":[{"key":"2025100315470200800_c1","doi-asserted-by":"publisher","first-page":"200","DOI":"10.3389\/fnins.2010.00200","article-title":"Modular and hierarchically modular organization of brain networks","volume":"4","year":"2010","journal-title":"Front. Neurosci."},{"key":"2025100315470200800_c2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2014.07.013","article-title":"Mathematical modeling for evolution of heterogeneous modules in the brain","volume":"62","year":"2014","journal-title":"Neural Networks"},{"key":"2025100315470200800_c3","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1385\/NI:2:3:353","article-title":"Clustered organization of cortical connectivity","volume":"2","year":"2004","journal-title":"Neuroinformatics"},{"key":"2025100315470200800_c4","doi-asserted-by":"publisher","first-page":"2407","DOI":"10.1093\/cercor\/bhl149","article-title":"Small-world anatomical networks in the human brain revealed by cortical thickness from MRI","volume":"17","year":"2007","journal-title":"Cereb. Cortex"},{"key":"2025100315470200800_c5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neulet.2003.10.063","article-title":"Functional connectivity patterns of human magnetoencephalographic recordings: A \u201csmall-world\u201d network?","volume":"355","year":"2004","journal-title":"Neurosci. Lett."},{"key":"2025100315470200800_c6","doi-asserted-by":"publisher","first-page":"018102","DOI":"10.1103\/PhysRevLett.94.018102","article-title":"Scale-free brain functional networks","volume":"94","year":"2005","journal-title":"Phys. Rev. Lett."},{"key":"2025100315470200800_c7","doi-asserted-by":"publisher","first-page":"e597","DOI":"10.1371\/journal.pone.0000597","article-title":"Mapping human whole-brain structural networks with diffusion MRI","volume":"2","year":"2007","journal-title":"PLoS One"},{"key":"2025100315470200800_c8","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1093\/cercor\/bhn047","article-title":"A small world of neuronal synchrony","volume":"18","year":"2008","journal-title":"Cereb. Cortex"},{"key":"2025100315470200800_c9","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of small-world networks","volume":"393","year":"1998","journal-title":"Nature"},{"key":"2025100315470200800_c10","volume-title":"Psychophysics: Introduction to its Perceptual Neural and Social Prospects","year":"1975"},{"key":"2025100315470200800_c11","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1044\/jshr.2503.473","article-title":"Parameters of auditory feedback","volume":"25","year":"1982","journal-title":"J. Speech, Language, Hearing Res."},{"key":"2025100315470200800_c12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S0303-2647(00)00116-7","article-title":"Spiking frequency versus odorant concentration in olfactory receptor neurons","volume":"58","year":"2000","journal-title":"Biosystems"},{"key":"2025100315470200800_c13","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1046\/j.1460-9568.2003.02766.x","article-title":"Relation between stimulus and response in frog olfactory receptor neurons in vivo","volume":"18","year":"2003","journal-title":"Eur. J. Neurosci."},{"key":"2025100315470200800_c14","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1162\/089976699300016188","article-title":"Concentration tuning mediated by spare receptor capacity in olfactory sensory neurons: A theoretical study","volume":"11","year":"1999","journal-title":"Neural Comput."},{"key":"2025100315470200800_c15","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1523\/JNEUROSCI.23-04-01506.2003","article-title":"Slow Na+ inactivation and variance adaptation in salamander retinal ganglion cells","volume":"23","year":"2003","journal-title":"J. Neurosci."},{"key":"2025100315470200800_c16","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/S0896-6273(02)01046-2","article-title":"Connexin36 is essential for transmission of rod-mediated visual signals in the mammalian retina","volume":"36","year":"2002","journal-title":"Neuron"},{"key":"2025100315470200800_c17","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.physa.2014.05.069","article-title":"Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses","volume":"410","year":"2014","journal-title":"Physica A"},{"key":"2025100315470200800_c18","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.cnsns.2013.06.003","article-title":"Dynamic range in a neuron network with electrical and chemical synapses","volume":"19","year":"2014","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"2025100315470200800_c19","doi-asserted-by":"publisher","first-page":"e1004372","DOI":"10.1371\/journal.pcbi.1004372","article-title":"Do brain networks evolve by maximizing their information flow capacity?","volume":"11","year":"2015","journal-title":"PLoS Comput. Biol."},{"key":"2025100315470200800_c20","doi-asserted-by":"publisher","first-page":"e1001066","DOI":"10.1371\/journal.pcbi.1001066","article-title":"Structural properties of the \u201cCaenorhabditis elegans\u201d neuronal network","volume":"7","year":"2011","journal-title":"PLoS Comput. Biol."},{"key":"2025100315470200800_c21","unstructured":"See http:\/\/cmtk.org\/viewer\/datasets\/ for the connectome of the C. elegans soil worm we have used in this work, 2010\u20132011 (Online, last accessed 27-February-2014)."},{"key":"2025100315470200800_c22","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1002\/cne.21974","article-title":"Equal number of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain","volume":"513","year":"2009","journal-title":"J. Comp. Neurol."},{"key":"2025100315470200800_c23","doi-asserted-by":"publisher","first-page":"036203","DOI":"10.1103\/PhysRevE.82.036203","article-title":"Combined effect of chemical and electrical synapses in Hindmarsh-Rose neural networks on synchronization and the rate of information","volume":"82","year":"2010","journal-title":"Phys. Rev. E"},{"key":"2025100315470200800_c24","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1098\/rspb.1984.0024","article-title":"A model of neuronal bursting using three coupled first order differential equations","volume":"221","year":"1984","journal-title":"Proc. R. Soc. London, Ser. B"},{"key":"2025100315470200800_c25","doi-asserted-by":"publisher","first-page":"128101","DOI":"10.1103\/PhysRevLett.91.128101","article-title":"Essential role of couplings between hearing nonlinearities","volume":"91","year":"2003","journal-title":"Phys. Rev. Lett."},{"key":"2025100315470200800_c26","unstructured":"See supplementary material at http:\/\/dx.doi.org\/10.1063\/1.4939837E-CHAOEH-26-001601 for the parameter spaces of the dynamic ranges and Steven law exponents for all six communities and the whole brain dynamical network."},{"key":"2025100315470200800_c27","first-page":"284","volume-title":"Computing Communities in Large Networks using Random Walks","year":"2005"},{"key":"2025100315470200800_c28","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0960-9822(93)90331-H","article-title":"The connectional organization of neural systems in the cat cerebral cortex","volume":"3","year":"1993","journal-title":"Current Biol."},{"key":"2025100315470200800_c29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11571-012-9219-8","article-title":"The neural binding problem(s)","volume":"7","year":"2013","journal-title":"Cognitive Neurodyn."},{"key":"2025100315470200800_c30","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s10827-007-0038-6","article-title":"Simulation of networks of spiking neurons: A review of tools and strategies","volume":"23","year":"2007","journal-title":"J. Comput. Neurosci."},{"key":"2025100315470200800_c31","volume-title":"Spiking Neurons Models","year":"2002"}],"container-title":["Chaos: An Interdisciplinary Journal of Nonlinear Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/1.4939837\/20731946\/013102_1_1.4939837.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/1.4939837\/20731946\/013102_1_1.4939837.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T19:47:11Z","timestamp":1759520831000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/cha\/article\/26\/1\/013102\/134572\/Dynamic-range-in-the-C-elegans-brain-network"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,1]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,1,1]]}},"URL":"https:\/\/doi.org\/10.1063\/1.4939837","relation":{},"ISSN":["1054-1500","1089-7682"],"issn-type":[{"value":"1054-1500","type":"print"},{"value":"1089-7682","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2016,1]]},"published":{"date-parts":[[2016,1,1]]},"article-number":"013102"}}