Computational Modeling of Spinal Locomotor Circuitry in the Age of Molecular Genetics

Abstract

Neuronal circuits in the spinal cord are essential for the control of locomotion. They integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. For several decades, computational modeling has complemented experimental studies by providing a mechanistic rationale for experimental observations and by deriving experimentally testable predictions. This symbiotic relationship between experimental and computational approaches has resulted in numerous fundamental insights. With recent advances in molecular and genetic methods, it has become possible to manipulate specific constituent elements of the spinal circuitry and relate them to locomotor behavior. This has led to computational modeling studies investigating mechanisms at the level of genetically defined neuronal populations and their interactions. We review literature on the spinal locomotor circuitry from a computational perspective. By reviewing examples leading up to and in the age of molecular genetics, we demonstrate the importance of computational modeling and its interactions with experiments. Moving forward, neuromechanical models with neuronal circuitry modeled at the level of genetically defined neuronal populations will be required to further unravel the mechanisms by which neuronal interactions lead to locomotor behavior.

Keywords: central pattern generator; computational modeling; interneurons; neuronal control of locomotion; sensory feedback; spinal cord.

Figure 1

Conceptual overview of computational models of spinal locomotor circuitry. (a,b) Model schematics of two rhythm generators coupled by commissural interneurons (CINs), controlling and coordinating left and right rhythmic activities—based on classical experimental studies (a) and incorporating molecular genetic data (b). Experimental data of genetically modified animals allowed for models to disentangle the network of commissural interneurons coupling the rhythm generators on each side of the spinal cord. (c) Integrated models of neuronal locomotor circuits with the musculoskeletal system are needed to simulate molecular genetic manipulations in in vivo experiments and mechanistically relate them to behavioral changes. Schematic in (b) has been adapted from Danner et al. [10].

Figure 2

Outline of the suggested process of interactions between computational modeling and experimental studies. First, a set of experimental data informs the development of the initial computational model. This model should reproduce the key findings of the experimental studies. Predictions can then be generated by simulating experimentally testable conditions and manipulations that were not part of the initial set of experimental data. Next, these modeling predictions can be tested experimentally: experimental falsification of a modeling prediction will provide valuable information to refine the model; their confirmation will improve the validity of the model. When applied iteratively, this process provides an explicit and consistent theoretical framework for experimentation, thereby reducing the number of necessary experiments while simultaneously increasing the information gained per experiment.