A framework for simulating axon guidance

Axon guidance and neuronal migration research in China

Proper migration of neuronal somas and axonal growth cones to designated locations in the developing brain is essential for the assembly of functional neuronal circuits. Rapid progress in research of axon guidance and neuronal migration has been made in the last twenty years. Chinese researchers began their exploration in this field ten years ago and have made significant contributions in clarifying the signal transduction of axon guidance and neuronal migration. Several unique experimental approaches, including the migration assay of single isolated neurons in response to locally delivered guidance cues, have been developed by Chinese neuroscientists to investigate the molecular machinery underlying these guidance events.

Non-parametric algorithmic generation of neuronal morphologies

Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-NEURON: and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.