Generators (CPGs) are being used for controlling the complicated gaits and timing
required for stable walking. Traditionally, these models are precisely designed
for oscillation at a set of specific frequencies and phase relationships, which while
easier to design is not conducive to robust and stable walking.
In recent years, work has been done on designing adaptive models of CPGs.
These CPGs are able to exhibit complex behaviors such as learning the resonant
dynamics of a system to improve walking stability, as well as using mathematical
learning rules to learn arbitrary signals and embed their relationships
within the system.
This work explores the possibility of implementing an adaptive frequency
CPG with a similar behavior to these systems, using conductance-based models
of dynamic non-spiking neurons connected as a synthetic nervous system
(SNS).
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Neuromodulation of central pattern generators and its role in the functional recovery of central pattern generator activity
Neuromodulators play an important role in how the nervous system organizes activity that results in behavior. Disruption of the normal patterns of neuromodulatory release or production is known to be related to the onset of severe pathologies such as Parkinson’s disease, Rett syndrome, Alzheimer’s disease, and affective disorders. Some of these pathologies involve neuronal structures that are called central pattern generators (CPGs), which are involved in the production of rhythmic activities throughout the nervous system. Here I discuss the interplay between CPGs and neuromodulatory activity, with particular emphasis on the potential role of neuromodulators in the recovery of disrupted neuronal activity. I refer to invertebrate and vertebrate model systems and some of the lessons we have learned from research on these systems and propose a few avenues for future research. I make one suggestion that may guide future research in the field: neuromodulators restrict the parameter landscape in which CPG components operate, and the removal of neuromodulators may enable a perturbed CPG in finding a new set of parameter values that can allow it to regain normal function.
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- Award ID(s):
- 1715808
- NSF-PAR ID:
- 10197342
- Date Published:
- Journal Name:
- Journal of Neurophysiology
- Volume:
- 122
- Issue:
- 1
- ISSN:
- 0022-3077
- Page Range / eLocation ID:
- 300 to 315
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Mammalian locomotion is generated by central pattern generators (CPGs) in the spinal cord, which produce alternating flexor and extensor activities controlling the locomotor movements of each limb. Afferent feedback signals from the limbs are integrated by the CPGs to provide adaptive control of locomotion. Responses of CPG-generated neural activity to afferent feedback stimulation have been previously studied during fictive locomotion in immobilized cats. Yet, locomotion in awake, behaving animals involves dynamic interactions between central neuronal circuits, afferent feedback, musculoskeletal system, and environment. To study these complex interactions, we developed a model simulating interactions between a half-center CPG and the musculoskeletal system of a cat hindlimb. Then, we analyzed the role of afferent feedback in the locomotor adaptation from a dynamic viewpoint using the methods of dynamical systems theory and nullcline analysis. Our model reproduced limb movements during regular cat walking as well as adaptive changes of these movements when the foot steps into a hole. The model generates important insights into the mechanism for adaptive locomotion resulting from dynamic interactions between the CPG-based neural circuits, the musculoskeletal system, and the environment.more » « less
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ABSTRACT Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) – systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers – and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.
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Hunt, A ; Vouloutsi, V. ; Moses, K. ; Quinn R. ; Mura, A. ; Prescott T. ; Verschure, P. (Ed.)Central pattern generators (CPGs) are ubiquitous neural circuits that contribute to an eclectic collection of rhythmic behaviors across an equally diverse assortment of animal species. Due to their prominent role in many neuromechanical phenomena, numerous bioinspired robots have been designed to both investigate and exploit the operation of these neural oscillators. In order to serve as effective tools for these robotics applications, however, it is often necessary to be able to adjust the phase alignment of multiple CPGs during operation. To achieve this goal, we present the design of our phase difference control (PDC) network using a functional subnetwork approach (FSA) wherein subnetworks that perform basic mathematical operations are assembled such that they serve to control the relative phase lead/lag of target CPGs. Our PDC network operates by first estimating the phase difference between two CPGs, then comparing this phase difference to a reference signal that encodes the desired phase difference, and finally eliminating any error by emulating a proportional controller that adjusts the CPG oscillation frequencies. The architecture of our PDC network, as well as its various parameters, are all determined via analytical design rules that allow for direct interpretability of the network behavior. Simulation results for both the complete PDC network and a selection of its various functional subnetworks are provided to demonstrate the efficacy of our methodology.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1152/jn.00784.2018
