Abstract
Dendritic morphology has been shown to have a dramatic impact on neuronal function. However, population features such as the inherent variability in dendritic morphology between cells belonging to the same neuronal type are often overlooked when studying computation in neural networks. While detailed models for morphology and electrophysiology exist for many types of single neurons, the role of detailed single cell morphology in the population has not been studied quantitatively or computationally. Here we use the structural context of the neural tissue in which dendritic trees exist to drive their generation in silico. We synthesize the entire population of dentate gyrus granule cells, the most numerous cell type in the hippocampus, by growing their dendritic trees within their characteristic dendritic fields bounded by the realistic structural context of (1) the granule cell layer that contains all somata and (2) the molecular layer that contains the dendritic forest. This process enables branching statistics to be linked to larger scale neuroanatomical features. We find large differences in dendritic total length and individual path length measures as a function of location in the dentate gyrus and of somatic depth in the granule cell layer. We also predict the number of unique granule cell dendrites invading a given volume in the molecular layer. This work enables the complete population-level study of morphological properties and provides a framework to develop complex and realistic neural network models.
Highlights
Growing evidence for the importance of dendritic structure on neuronal function has inspired the construction of morphologically realistic computational models of single neurons
Parametric 2D manifolds were fitted to these boundary surfaces (Figure 1A; see Methods for detailed equations) in order to provide a coordinate system in which depth in the granule cell layer (GCL) and molecular layer (ML) as well as the septo-temporal and infra- versus suprapyramidal axes are mapped
This in turn enabled the subdivision of the ML volume into inner (IML), middle (MML), and outer molecular layers (OML) using intermediate surfaces, since several aspects of granule cells (GCs) morphology have previously been associated to these reference structures
Summary
Growing evidence for the importance of dendritic structure on neuronal function has inspired the construction of morphologically realistic computational models of single neurons. The intra-class morphological variability could have a significant impact on the integration of individual neurons into the circuit and their resulting role in network computation. This has led to the development of detailed three-dimensional morphological reconstructions of single cells [7] and functional models incorporating this level of detail [8,9]. Does the incorporation of realistic morphology enable more accurate reproduction of measured electrophysiology, it allows for a more detailed representation of network connectivity These together enable a better understanding of the underlying computation in the network. Since experimental reconstructions are to date available only in small sample sizes, techniques to generate population-level morphological models will require the amplification of these data sets to fully realistic and diverse populations [15]
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