On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of single-neuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts.

The Eya1 Phosphatase Mediates Shh-Driven Symmetric Cell Division of Cerebellar Granule Cell Precursors

During neural development, stem and precursor cells can divide either symmetrically or asymmetrically. The transition between symmetric and asymmetric cell divisions is a major determinant of precursor cell expansion and neural differentiation, but the underlying mechanisms that regulate this transition are not well understood. Here, we identify the Sonic hedgehog (Shh) pathway as a critical determinant regulating the mode of division of cerebellar granule cell precursors (GCPs). Using partial gain and loss of function mutations within the Shh pathway, we show that pathway activation determines spindle orientation of GCPs, and that mitotic spindle orientation correlates with the mode of division. Mechanistically, we show that the phosphatase Eya1 is essential for implementing Shh-dependent GCP spindle orientation. We identify atypical protein kinase C (aPKC) as a direct target of Eya1 activity and show that Eya1 dephosphorylates a critical threonine (T410) in the activation loop. Thus, Eya1 inactivates aPKC, resulting in reduced phosphorylation of Numb and other components that regulate the mode of division. This Eya1-dependent cascade is critical in linking spindle orientation, cell cycle exit and terminal differentiation. Together these findings demonstrate that a Shh-Eya1 regulatory axis selectively promotes symmetric cell divisions during cerebellar development by coordinating spindle orientation and cell fate determinants.