Evolution: Vertebrate Limb Control over 420 Million Years

The Bcm rule allows a spinal cord model to learn rhythmic movements

Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.

Cholinergic Modulation of Locomotor Circuits in Vertebrates

Locomotion is a basic motor act essential for survival. Amongst other things, it allows animals to move in their environment to seek food, escape predators, or seek mates for reproduction. The neural mechanisms involved in the control of locomotion have been examined in many vertebrate species and a clearer picture is progressively emerging. The basic muscle synergies responsible for propulsion are generated by neural networks located in the spinal cord. In turn, descending supraspinal inputs are responsible for starting, maintaining, and stopping locomotion as well as for steering and controlling speed. Several neurotransmitter systems play a crucial role in modulating the neural activity during locomotion. For instance, cholinergic inputs act both at the spinal and supraspinal levels and the underlying mechanisms are the focus of the present review. Much information gained on supraspinal cholinergic modulation of locomotion was obtained from the lamprey model. Nicotinic cholinergic inputs increase the level of excitation of brainstem descending command neurons, the reticulospinal neurons (RSNs), whereas muscarinic inputs activate a select group of hindbrain neurons that project to the RSNs to boost their level of excitation. Muscarinic inputs also reduce the transmission of sensory inputs in the brainstem, a phenomenon that could help in sustaining goal directed locomotion. In the spinal cord, intrinsic cholinergic inputs strongly modulate the activity of interneurons and motoneurons to control the locomotor output. Altogether, the present review underlines the importance of the cholinergic inputs in the modulation of locomotor activity in vertebrates.

Whole Body Coordination for Self-Assistance in Locomotion

The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk). These body parts can exhibit specific coordination patterns with each other. In human walking, we found that the swing leg cooperates with the upper body and the stance leg in different ways (e.g., in-phase and out-of-phase in vertical and horizontal directions, respectively). Such patterns of self-assistance found in human locomotion could be of advantage in robotics design, in the design of any assistive device for patients with movement impairments. It can also shed light on several unexplained infrastructural features of the CNS motor control. Self-assistance means that distributed parts of the body contribute to an overlay of functions that are required to solve the underlying motor task. To draw advantage of self-assisting effects, precise and balanced spatiotemporal patterns of muscle activation are necessary. We show that the necessary neural connectivity infrastructure to achieve such muscle control exists in abundance in the spinocerebellar circuitry. We discuss how these connectivity patterns of the spinal interneurons appear to be present already perinatally but also likely are learned. We also discuss the importance of these insights into whole body locomotion for the successful design of future assistive devices and the sense of control that they could ideally confer to the user.

Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.

Corticospinal vs Rubrospinal Revisited: An Evolutionary Perspective for Sensorimotor Integration

The knowledge about how different subsystems participate and interplay in sensorimotor control is fundamental to understand motor deficits associated with CNS injury and movement recovery. The role of corticospinal (CS) and rubrospinal (RS) projections in motor control has been extensively studied and compared, and it is clear that both systems are important for skilled movement. However, during phylogeny, the emerging cerebral cortex took a higher hierarchical role controlling rubro-cerebellar circuits. Here, we present anatomical, neurophysiological, and behavioral evidence suggesting that both systems modulate complex segmental neuronal networks in a parallel way, which is important for sensorimotor integration at spinal cord level. We also highlight that, although specializations exist, both systems could be complementary and potentially subserve motor recovery associated with CNS damage.

Current Principles of Motor Control, with Special Reference to Vertebrate Locomotion

The vertebrate control of locomotion involves all levels of the nervous system from cortex to the spinal cord. Here, we aim to cover all main aspects of this complex behavior, from the operation of the microcircuits in the spinal cord to the systems and behavioral levels and extend from mammalian locomotion to the basic undulatory movements of lamprey and fish. The cellular basis of propulsion represents the core of the control system, and it involves the spinal central pattern generator networks (CPGs) controlling the timing of different muscles, the sensory compensation for perturbations, and the brain stem command systems controlling the level of activity of the CPGs and the speed of locomotion. The forebrain and in particular the basal ganglia are involved in determining which motor programs should be recruited at a given point of time and can both initiate and stop locomotor activity. The propulsive control system needs to be integrated with the postural control system to maintain body orientation. Moreover, the locomotor movements need to be steered so that the subject approaches the goal of the locomotor episode, or avoids colliding with elements in the environment or simply escapes at high speed. These different aspects will all be covered in the review.

A kinematic synergy for terrestrial locomotion shared by mammals and birds

Locomotion of tetrapods on land adapted to different environments and needs resulting in a variety of different gait styles. However, comparative analyses reveal common principles of limb movement control. Here, we report that a kinematic synergy involving the planar covariation of limb segment motion holds in 54 different animal species (10 birds and 44 mammals), despite large differences in body size, mass (ranging from 30 g to 4 tonnes), limb configuration, and amplitude of movements. This kinematic synergy lies at the interface between the neural command signals output by locomotor pattern generators, the mechanics of the body center of mass and the external environment, and it may represent one neuromechanical principle conserved in evolution to save mechanical energy.

Animals have evolved very different body shapes and styles of movement that are adapted to their needs in the habitats they live in. For example, mice, lions and many other animals use four limbs to walk, while humans and birds only use two limbs.

The styles animals use to walk also differ in terms of how long each foot is on the ground during a single stride, and for four-legged animals, in how long a forefoot lags behind the hindfoot on the same side of the body during the stride. Yet, there are general principles in how walking is organized that are shared between animals of vastly different shapes and sizes. Many animals save energy during walking by swinging the center of their body mass back and forth like a pendulum.

Networks of neurons are responsible for controlling how and when animals move, and these networks have similar architectures and patterns of activity in many different mammals and birds. How do signals from the nervous system regulate the position of the center of body mass while an animal walks?

Here, Catavitello et al. addressed this question by analyzing how over 50 different species of birds and mammals walked around in zoo enclosures and other semi-natural or natural environments. The species studied ranged in size from mice weighing around 30 grams to elephants weighing around 4 tonnes. The team also studied human volunteers walking on treadmills.

The experiments show that all the species studied coordinate their limbs in the same way, so that the angle to which a particular segment of a limb can bend varies together with the angles that the other limb segments bend. This coordination implies that the movement of the center of body mass is regulated and energy is saved.

Along with providing new insight into how walking evolved, these findings may aid research into new approaches to treat walking impairments in humans and other animals.

Walking with perturbations: a guide for biped humans and robots

This paper provides an update on the neural control of bipedal walking in relation to bioinspired models and robots. It is argued that most current models or robots are based on the construct of a symmetrical central pattern generator (CPG). However, new evidence suggests that CPG functioning is basically asymmetrical with its flexor half linked more tightly to the rhythm generator. The stability of bipedal gait, which is an important problem for robots and biological systems, is also addressed. While it is not possible to determine how biological biped systems guarantee stability, robot solutions can be useful to propose new hypotheses for biology. In the second part of this review, the focus is on gait perturbations, which is an important topic in robotics in view of the frequent falls of robots when faced with perturbations. From the human physiology it is known that the initial reaction often consists of a brief interruption followed by an adequate response. For instance, the successful recovery from a trip is achieved using some basic reactions (termed elevating and lowering strategies), that depend on the phase of the step cycle of the trip occurrence. Reactions to stepping unexpectedly in a hole depend on comparing expected and real feedback. Implementation of these ideas in models and robotics starts to emerge, with the most advanced robots being able to learn how to fall safely and how to deal with complicated disturbances such as provided by walking on a split-belt.