Morphological properties of neurons affect statistics of neuronal network connectivity leading to functionally different network types
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
|Title of host publication||Neuroscience 2012; 42nd Annual Meeting, New Orleans, USA, October 14-18, 2012|
|Publisher||Society for Neuroscience|
|Publication status||Published - 13 Oct 2012|
|Publication type||A4 Article in a conference publication|
|Event||The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012 - New Orleans, United States|
Duration: 14 Oct 2012 → 18 Oct 2012
|Conference||The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012|
|Period||14/10/12 → 18/10/12|
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.