An organotypic slice culture to study the formation of calyx of Held synapses in-vitro.

The calyx of Held, a large axo-somatic relay synapse containing hundreds of presynaptic active zones, is possibly the largest nerve terminal in the mammalian CNS. Studying its initial growth in-vitro might provide insights into the specification of synaptic connection size in the developing brain. However, attempts to maintain calyces of Held in organotypic cultures have not been fruitful in past studies. Here, we describe an organotypic slice culture method in which calyces of Held form in-vitro. We made coronal brainstem slices with an optimized slice angle using newborn mice in which calyces have not yet formed; the presynaptic bushy cells were genetically labeled using the Math5 promoter. After six to nine days of culturing, we readily observed large Math5-positive nerve terminals in the medial nucleus of the trapezoid body (MNTB), but not in the neighboring lateral superior olive nucleus (LSO). These calyx-like synapses expressed the Ca2+- sensor Synaptotagmin-2 (Syt-2) and the Ca2+ binding protein Parvalbumin (PV), two markers of developing calyces of Held in vivo. Application of the BMP inhibitor LDN-193189 significantly inhibited the growth of calyx synapses, demonstrating the feasibility of long-term pharmacological manipulation using this organotypic culture method. These experiments provide a method for organotypic culturing of calyces of Held, and show that the formation of calyx-like synapses onto MNTB neurons can be preserved in-vitro. Furthermore, our study adds pharmacological evidence for a role of BMP-signaling in the formation of large calyx of Held synapses

Incremental Motion Reshaping of Autonomous Dynamical Systems

This paper presents an approach to incrementally learn a reshaping term that modifies the trajectories of an autonomous dynamical system without affecting its stability properties. The reshaping term is considered as an additive control input and it is incrementally learned from human demonstrations using Gaussian process regression. We propose a novel parametrization of this control input that preserves the time-independence and the stability of the reshaped system, as analytically proved in the performed Lyapunov stability analysis. The effectiveness of the proposed approach is demonstrated with simulations and experiments on a real robot