## Abstrakti

Connectivity in a network of neurons significantly affects its capacity to store and transmit information. But, even in a medium-scale network, mapping every connection is difficult or even impossible task (Sporns, 2005). Instead, statistical measures can be used to characterize network connectivity and the possible impact on its functionality. In this work, we examined how the properties of individual neuron morphology affect the relevant connectivity measures, including clustering coefficient, shortest path length, and motif counts. The first two measures capture global properties of network organization while the last one describes connectivity locally. Morphology of neurons is described by the geometry of its neurites, how they branch, elongate and cover the space. We studied two-dimensional computational models of neuronal networks that correspond to dissociated neocortical cell cultures.

We are interested in two questions. First, which properties of neuron morphology significantly influence network structure? Under what conditions the specific network types, e.g locally connected or small-world networks, emerge? Second, how much the precision of neurite morphology description affects global and local network properties?

In order to answer these questions, we analyze two neuronal network models. First one, which we call ‘space covering model’, is composed of neurons with less detailed morphology. Each neurite is represented by an ellipsoid field and by the distribution of neurite segments within that field (Snider etal, 2010). Its low dimensionality makes it possible to examine the parameter space for relatively large networks. Such networks are similar to the ones considered in (Herzog 2007; Voges, 2010) that exhibit small-world connectivity while minimizing the wiring cost. Our study provides a possibility to closer relate morphology and connectivity. The second network model, the ‘detailed model’, employs the neurite description from (Van Pelt, 2003) that incorporates details of morphology and is simulated using NETMORPH (Koene, 2009). In both models, synapses are formed with certain probability between each proximal axon-dendrite pair.

Using the ‘space covering’ model we first examine networks of one nonspecific type of neurons, and then the networks of two most frequent types of neurons in cultures, pyramidal and GABAergic neurons. Next, we compare space covering and detailed model. We set conditions when the neurons of the detailed model can be mapped into neurons of the space covering model. Then, we compare the connectivity measures computed for the two models.

We are interested in two questions. First, which properties of neuron morphology significantly influence network structure? Under what conditions the specific network types, e.g locally connected or small-world networks, emerge? Second, how much the precision of neurite morphology description affects global and local network properties?

In order to answer these questions, we analyze two neuronal network models. First one, which we call ‘space covering model’, is composed of neurons with less detailed morphology. Each neurite is represented by an ellipsoid field and by the distribution of neurite segments within that field (Snider etal, 2010). Its low dimensionality makes it possible to examine the parameter space for relatively large networks. Such networks are similar to the ones considered in (Herzog 2007; Voges, 2010) that exhibit small-world connectivity while minimizing the wiring cost. Our study provides a possibility to closer relate morphology and connectivity. The second network model, the ‘detailed model’, employs the neurite description from (Van Pelt, 2003) that incorporates details of morphology and is simulated using NETMORPH (Koene, 2009). In both models, synapses are formed with certain probability between each proximal axon-dendrite pair.

Using the ‘space covering’ model we first examine networks of one nonspecific type of neurons, and then the networks of two most frequent types of neurons in cultures, pyramidal and GABAergic neurons. Next, we compare space covering and detailed model. We set conditions when the neurons of the detailed model can be mapped into neurons of the space covering model. Then, we compare the connectivity measures computed for the two models.

Alkuperäiskieli | Englanti |
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Otsikko | Neuroscience 2012; 42nd Annual Meeting, New Orleans, USA, October 14-18, 2012 |

Kustantaja | Society for Neuroscience |

Tila | Julkaistu - 13 lokak. 2012 |

OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |

Tapahtuma | The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012 - New Orleans, Yhdysvallat Kesto: 14 lokak. 2012 → 18 lokak. 2012 |

### Conference

Conference | The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012 |
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Maa/Alue | Yhdysvallat |

Kaupunki | New Orleans |

Ajanjakso | 14/10/12 → 18/10/12 |