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Modeling the Organization of Spinal Cord Neural Circuits Controlling Two-Joint Muscles
Book chapter

Modeling the Organization of Spinal Cord Neural Circuits Controlling Two-Joint Muscles

Natalia A. Shevtsova, Khaldoun Hamade, Samit Chakrabarty, Sergey N. Markin, Boris I. Prilutsky and Ilya A. Rybak
Neuromechanical Modeling of Posture and Locomotion, pp 121-162
01 Jan 2016

Abstract

Life Sciences & Biomedicine Mathematical & Computational Biology Neurosciences Neurosciences & Neurology Science & Technology
The activity of most motoneurons controlling one-joint muscles during locomotion are locked to either extensor or flexor phase of locomotion. In contrast, bifunctional motoneurons, controlling two-joint muscles such as posterior biceps femoris and semitendinosus (PBSt) or rectus femoris (RF), express a variety of activity patterns including firing bursts during both locomotor phases, which may depend on locomotor conditions. Although afferent feedback and supraspinal inputs significantly contribute to shaping the activity of PBSt and RF motoneurons during real locomotion, these motoneurons show complex firing patterns and variable behaviors under the conditions of fictive locomotion in the immobilized decerebrate cat, i.e., with a lack of patterned supraspinal and afferent inputs. This suggests that firing patterns of PBSt and RF motoneurons are defined by neural interactions inherent to the locomotor central pattern generator (CPG) within the spinal cord. In this study, we use computational modeling to suggest the architecture of spinal circuits representing the locomotor CPG and the connectivity pattern of spinal interneurons defining the behavior of bifunctional PBSt and RF motoneurons. The proposed model reproduces the complex firing patterns of these motoneurons during fictive locomotion under different conditions including spontaneous deletions of flexor and extensor activities and provides insights into the organization of spinal circuits controlling locomotion in mammals.

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Web of Science research areas
Mathematical & Computational Biology
Neurosciences
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