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Revista Brasileira de Engenharia Biomédica

versión ISSN 1517-3151


ELIAS, Leonardo Abdala  y  KOHN, André Fabio. Web-based neuromuscular simulator applied to the teaching of principles of neuroscience. Rev. Bras. Eng. Bioméd. [online]. 2013, vol.29, n.3, pp.213-226. ISSN 1517-3151.

INTRODUCTION: The learning of core concepts in neuroscience can be reinforced by a hands-on approach, either experimental or computer-based. In this work, we present a web-based multi-scale neuromuscular simulator that is being used as a teaching aid in a campus-wide course on the Principles of Neuroscience. METHODS: The simulator has several built-in individual models based on cat and human biophysics, which are interconnected to represent part of the neuromuscular system that controls leg muscles. Examples of such elements are i) single neurons, representing either motor neurons or interneurons mediating reciprocal, recurrent and Ib inhibition; ii) afferent fibers that can be stimulated to generate spinal reflexes; iii) muscle unit models, generating force and electromyogram; and iv) stochastic inputs, representing the descending volitional motor drive. RESULTS: Several application examples are provided in the present report, ranging from studies of individual neuron responses to the collective action of many motor units controlling muscle force generation. A subset of them was included in an optional homework assignment for Neuroscience and Biomedical Engineering graduate students enrolled in the course cited above at our University. Almost all students rated the simulator as a good or an excellent learning tool, and approximately 90% declared that they would use the simulator in future projects. CONCLUSION: The results allow us to conclude that multi-scale neuromuscular simulator is an effective teaching tool. Special features of this free teaching resource are its direct usability from any browser (, its user-friendly graphical user interface (GUI) and the preset demonstrations.

Palabras clave : Biomedical engineering education; Biological system modeling; Computational biology; Neural engineering.

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