Name
Abstract | ||
|---|---|---|
One of the main challenges in neuroscience is to define the detailed structural design of the nervous system. This challenge is one of the first steps towards understanding how neural circuits contribute to the functional organization of the nervous system. In the cerebral cortex pyramidal neurons are key elements in brain function as they represent the most abundant cortical neuronal type and the main source of cortical excitatory synapses. Therefore, many researchers are interested in the analysis of the microanatomy of pyramidal cells since it constitutes an excellent tool for better understanding cortical processing of information. Computational models of neuronal networks based on real cortical circuits have become useful tools for studying certain aspects of the functional organization of the neocortex. Neuronal morphologies (morphological models) represent key features in these functional models. For these purposes, synthetic or virtual dendritic trees can be generated through a morphological model of a given neuronal type based on real morphometric parameters obtained from intracellularly-filled single neurons. This paper presents a new method to construct virtual dendrites by means of sampling a branching model that represents the dendritic morphology. This method has been contrasted using complete basal dendrites from 374 layer II/III pyramidal neurons of the mouse neocortex. |
Year | Venue | Keywords |
|---|---|---|
2010 | IEA/AIE (3) | functional organization,better understanding cortical processing,functional model,nervous system,cerebral cortex pyramidal neuron,iii pyramidal neuron,morphological model,synthetic dendrites,abundant cortical neuronal type,cortical excitatory synapsis,real cortical circuit,computer model,machine learning,microstructures,neuronal morphology,neuronal network |
Field | DocType | Volume |
Neocortex,Synapse,Neuroscience,Cortical neurons,Computer science,Excitatory postsynaptic potential,Computational model,Nervous system,Cerebral cortex,Biological neural network | Conference | 6098 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-13032-1 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaime Fernández | 1 | 0 | 0.34 |
| Laura Fernández | 2 | 0 | 0.34 |
| Ruth Benavides-Piccione | 3 | 19 | 3.86 |
| Inmaculada Ballesteros-Yañez | 4 | 0 | 0.34 |
| Javier De Felipe | 5 | 76 | 8.74 |
| José-María Peña | 6 | 137 | 9.97 |