Bmp6, a member of the 60A subgroup of bone morphogenetic proteins (BMPs), is expressed in diverse sites in the developing mouse embryo from preimplantation stages onwards. To evaluate roles for Bmp6 signaling in vivo, gene targeting was used to generate a null mutation at the Bmp6 locus. The resulting Bmp6 mutant mice are viable and fertile, and show no overt defects in tissues known to express Bmp6 mRNA. The skeletal elements of newborn and adult mutants are indistinguishable from wild-type. However, careful examination of skeletogenesis in late gestation embryos reveals a consistent delay in ossification strictly confined to the developing sternum. In situ hybridization studies in the developing long bones and sternum show that other BMP family members are expressed in overlapping domains. In particular we find that Bmp2 and Bmp6 are coexpressed in hypertrophic cartilage, suggesting that Bmp2 may functionally compensate in Bmp6 null mice. The defects in sternum development in Bmp6 null mice are likely to be associated with a transient early expression of Bmp6 in the sternal bands, prior to ossification. These sternal defects are slightly exacerbated in Bmp5/6 double mutant animals.

No abstract

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.