Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer, Henrik Jörntell
{"title":"The Bcm rule allows a spinal cord model to learn rhythmic movements.","authors":"Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer, Henrik Jörntell","doi":"10.1007/s00422-023-00970-z","DOIUrl":null,"url":null,"abstract":"<p><p>Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"275-284"},"PeriodicalIF":1.7000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600281/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological Cybernetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00422-023-00970-z","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/18 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.
期刊介绍:
Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.