MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology. At the lowest level, it offers support for the implementation of sparse networks. For example, the library SparseImplementationLib supports sparse random networks

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Studying imitation learning of long sequences requires the evaluation of inaccurately and incompletely reproduced movement sequences. In order to evaluate the movement reproduction, it has to be assigned to the original stimulus. We developed an assignment algorithm that considers the Spatial Neighborhood and Order of reproduction (SNOA). To evaluate the features of this analysis it was applied to human performance during learning of long pointing sequences under two conditions: stimulus-guided reproduction with high spatial accuracy and imitation learning with low spatial accuracy. The results were compared with a simple assignment considering Spatial Neighborhood only (SNA) and with a Manual Assignment (MA). In the stimulus-guided reproduction the error measures did not differ between the algorithms. In contrast, with imitation learning, SNOA and MA generated higher estimates of order and omission errors than SNA. The results show that SNOA can be used to automatically quantify the similarity of both movement structure and metric information between long target sequences and inaccurate and incomplete movement reproductions.

A common challenge to Prognostic Health Management (PHM) systems is the management of data across different organizations based on a standardized format and meaning. The Open System Architecture for Condition-based Maintenance (OSA-CBM) and the Open System Architecture for Enterprise Application Integration (OSA-EAI) are complementary reference architectures for domain-independent asset and condition data management. In previous papers, we reported on our experiences with implementing a data integration layer based on these two architectures. In this paper, we report on our experience implementing code generators for binary OSA-CBM and OSA-EAI Tech-CDE (Compound Document Exchange), and the utilization of the resulting components within the OMAHA project. OMAHA aims towards an overall management architecture for health analysis, incorporating manufacturers, operators and maintainers of fleets of aircraft.The OSA-CBM standard specifies a message structure but leaves the assembly and disassembly of OSA-CBM data up to the implementor. Our solution is a builder/reader Application Programming Interface (API) for a binary OSA-CBM message codec which we have implemented under the constraints of a real-time computing environment. The required C code is automatically generated from the provided technical documentation for OSA-CBM. We discuss the properties of the resulting codec and point out future improvements for the OSA-CBM binary protocol to improve consistency and to add the capability of streaming. Using the same generative approach we have implemented a code generator for a Tech-CDE-compliant middleware system, consisting ofclient libraries (currently C++ and Java), a network layer, a server portion, and a database backend. Analogously to OSA-CBM, the code generator processes the documentation provided for Tech-CDE, creating both productive and testing code. We discuss the properties of the resulting system, report specific limitations of the Tech-CDE protocol and suggest mitigations. The paper concludes with an experience report from utilizing our work in the OMAHA project. While Tech-CDE was generally found sufficient, we identified areas of improvement, including protocol properties and entity coverage. We were able to make customizations using our generative coding approach and present these as suggestions for future standard extensions.