Abstract
In this review, we are considering an actively developing tool in neuroscience—robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.
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Migalev, A.S., Vigasina, K.D. & Gotovtsev, P.M. A review of motor neural system robotic modeling approaches and instruments. Biol Cybern 116, 271–306 (2022). https://doi.org/10.1007/s00422-021-00918-1
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DOI: https://doi.org/10.1007/s00422-021-00918-1