Robustness, flexibility, and sensitivity in a multifunctional motor control model

Abstract

Motor systems must adapt to perturbations and changing conditions both within and outside the body. We refer to the ability of a system to maintain performance despite perturbations as “robustness,” and the ability of a system to deploy alternative strategies that improve fitness as “flexibility.” Different classes of pattern-generating circuits yield dynamics with differential sensitivities to perturbations and parameter variation. Depending on the task and the type of perturbation, high sensitivity can either facilitate or hinder robustness and flexibility. Here we explore the role of multiple coexisting oscillatory modes and sensory feedback in allowing multiphasic motor pattern generation to be both robust and flexible. As a concrete example, we focus on a nominal neuromechanical model of triphasic motor patterns in the feeding apparatus of the marine mollusk Aplysia californica. We find that the model can operate within two distinct oscillatory modes and that the system exhibits bistability between the two. In the “heteroclinic mode,” higher sensitivity makes the system more robust to changing mechanical loads, but less robust to internal parameter variations. In the “limit cycle mode,” lower sensitivity makes the system more robust to changes in internal parameter values, but less robust to changes in mechanical load. Finally, we show that overall performance on a variable feeding task is improved when the system can flexibly transition between oscillatory modes in response to the changing demands of the task. Thus, our results suggest that the interplay of sensory feedback and multiple oscillatory modes can allow motor systems to be both robust and flexible in a variable environment.

Rapid Adaptation to Changing Mechanical Load by Ordered Recruitment of Identified Motor Neurons

“…As we have shown, animals may extend the duration of the power stroke (retraction) of swallowing when load is encountered. This is consistent with predictions made by a nominal neuromechanical model of Aplysia feeding (Shaw et al, 2015;Lyttle et al, 2017). Similarly, in vertebrate and stick insect locomotion, increased load during the stance phase causes increased excitation to the leg extensor muscles (Pearson, 1995).…”

“…More generally, animals will associate different foods with different levels of reward and seek out their preferred foods (Petrovich, 2018). Many of the medium-and long-term behavioral adaptations that animals exhibit are forms of flexibility, through which they remain robust to environmental variation and maintain high fitness (Lyttle et al, 2017).…”

The infinitesimal phase response curves of oscillators in piecewise smooth dynamical systems

“…The third example (Section 3.3) is a 2-D Glass network, a PWL system obtained as the singular limit of a class of models for feedback inhibition and gene regulatory networks [19]. The fourth example (Section 3.4) is a 3-D PWL system arising as a simplification of a nominal central pattern generator model for regulation of feeding motor activity in the marine mollusk Aplysia californica [37,54] and related to a Lotka-Volterra system with three populations [44]. In the final two examples, we extend our theory to the case of weakly coupled oscillators (Section 4.1).…”

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

“…Much like many studies on flexibility [11,9,38] we can switch between these functionality easily, with either changes in synaptic weights constant inputs or changes in bias current. Again, we showed that while many of these functions can exist without mutual inhibition; they cannot coexist with other functions.…”