dorsal/arxiv
View SchemaExcitable Scale Free Networks
| Authors | Mauro Copelli, Paulo R. A. Campos |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0703004 |
| URL | https://arxiv.org/abs/q-bio/0703004 |
| DOI | 10.1140/epjb/e2007-00114-7 |
| Journal | Eur. Phys. J. B 56, 273-278 (2007) |
Abstract
When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range.
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"abstract": "When a simple excitable system is continuously stimulated by a Poissonian\nexternal source, the response function (mean activity versus stimulus rate)\ngenerally shows a linear saturating shape. This is experimentally verified in\nsome classes of sensory neurons, which accordingly present a small dynamic\nrange (defined as the interval of stimulus intensity which can be appropriately\ncoded by the mean activity of the excitable element), usually about one or two\ndecades only. The brain, on the other hand, can handle a significantly broader\nrange of stimulus intensity, and a collective phenomenon involving the\ninteraction among excitable neurons has been suggested to account for the\nenhancement of the dynamic range. Since the role of the pattern of such\ninteractions is still unclear, here we investigate the performance of a\nscale-free (SF) network topology in this dynamic range problem. Specifically,\nwe study the transfer function of disordered SF networks of excitable\nGreenberg-Hastings cellular automata. We observe that the dynamic range is\nmaximum when the coupling among the elements is critical, corroborating a\ngeneral reasoning recently proposed. Although the maximum dynamic range yielded\nby general SF networks is slightly worse than that of random networks, for\nspecial SF networks which lack loops the enhancement of the dynamic range can\nbe dramatic, reaching nearly five decades. In order to understand the role of\nloops on the transfer function we propose a simple model in which the density\nof loops in the network can be gradually increased, and show that this is\naccompanied by a gradual decrease of dynamic range.",
"arxiv_id": "q-bio/0703004",
"authors": [
"Mauro Copelli",
"Paulo R. A. Campos"
],
"categories": [
"q-bio.NC",
"cond-mat.dis-nn",
"nlin.CG",
"physics.bio-ph"
],
"doi": "10.1140/epjb/e2007-00114-7",
"journal_ref": "Eur. Phys. J. B 56, 273-278 (2007)",
"title": "Excitable Scale Free Networks",
"url": "https://arxiv.org/abs/q-bio/0703004"
},
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