Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion.

How behaviors are generated by neural circuits is one of the central questions in neurobiology. Under standard culture conditions, Caenorhabditis elegans travel by propagating sinusoidal waves, moving primarily forward, punctuated by brief runs of backing. How these behaviors are generated and altered is not well understood. Using a combination of behavioral analyses and neuronal imaging, I reveal that an activity imbalance between cholinergic A- and B-motoneurons is the key determinant of directional locomotion. Furthermore, heterotypic gap junctions that couple command interneurons and motoneurons of the backward motor circuit, mediated by innexins UNC-7 in AVA and UNC-9 in A-motoneurons, respectively, establish the B>A activity pattern required for forward movement. Loss of this coupling results in both the hyperactivation of AVA backward interneurons revealing the unregulated, endogenous activity of A-motoneurons. With equal A-motoneuron activity levels as B-motoneurons, innexin mutant animals exhibit irregular body bending (kinking) instead of executing forward motion, as well as increased backing. Through a genetic screen, I identified two stomatin-like proteins as regulators of innexin UNC-9 activity that affect C. elegans’ directional movement. The loss of function of stomatin-like unc-1 leads to the same kinker phenotype as unc-7 or unc-9 mutants. Like UNC-9, UNC-1 functions primarily in the A-motoneurons to allow forward motion, suggesting that UNC-1 is required for effective UNC-7-UNC-9 coupling between AVA and A-motoneurons. Dominant mutations in UNC-1, and another stomatin-like protein STO-6, exhibit genetic interactions with these innexin mutants. These mutations partially restore the forward movement of unc-7 mutants, in an UNC-9-dependent manner, indicating that they regulate UNC-9 channel activity in motoneurons to re-establish the B>A-motoneuron activity pattern in the absence of heterotypic gap junctions between interneurons and motoneurons. These studies describe a role of gap junctions as regulators of circuit dynamics by establishing an imbalanced motoneuron activity pattern that favors forward motion, which can be modulated by upper layer inputs. This study also identifies stomatin-like regulators of innexin hemichannel and gap junction function. Future work will focus on understanding mechanisms through which these stomatins regulate the activity of specific innexin channels in C. elegans motoneurons, as well as their contribution to the dynamic output of the C. elegans motor circuit.

No description available.

No description available.

How behaviors are generated by neural circuits is one of the central questions in neurobiology. Under standard culture conditions, Caenorhabditis elegans travel by propagating sinusoidal waves, moving primarily forward, punctuated by brief runs of backing. How these behaviors are generated and altered is not well understood. Using a combination of behavioral analyses and neuronal imaging, I reveal that an activity imbalance between cholinergic A- and B-motoneurons is the key determinant of directional locomotion. Furthermore, heterotypic gap junctions that couple command interneurons and motoneurons of the backward motor circuit, mediated by innexins UNC-7 in AVA and UNC-9 in A-motoneurons, respectively, establish the B>A activity pattern required for forward movement. Loss of this coupling results in both the hyperactivation of AVA backward interneurons revealing the unregulated, endogenous activity of A-motoneurons. With equal A-motoneuron activity levels as B-motoneurons, innexin mutant animals exhibit irregular body bending (kinking) instead of executing forward motion, as well as increased backing. Through a genetic screen, I identified two stomatin-like proteins as regulators of innexin UNC-9 activity that affect C. elegans’ directional movement. The loss of function of stomatin-like unc-1 leads to the same kinker phenotype as unc-7 or unc-9 mutants. Like UNC-9, UNC-1 functions primarily in the A-motoneurons to allow forward motion, suggesting that UNC-1 is required for effective UNC-7-UNC-9 coupling between AVA and A-motoneurons. Dominant mutations in UNC-1, and another stomatin-like protein STO-6, exhibit genetic interactions with these innexin mutants. These mutations partially restore the forward movement of unc-7 mutants, in an UNC-9-dependent manner, indicating that they regulate UNC-9 channel activity in motoneurons to re-establish the B>A-motoneuron activity pattern in the absence of heterotypic gap junctions between interneurons and motoneurons. These studies describe a role of gap junctions as regulators of circuit dynamics by establishing an imbalanced motoneuron activity pattern that favors forward motion, which can be modulated by upper layer inputs. This study also identifies stomatin-like regulators of innexin hemichannel and gap junction function. Future work will focus on understanding mechanisms through which these stomatins regulate the activity of specific innexin channels in C. elegans motoneurons, as well as their contribution to the dynamic output of the C. elegans motor circuit.

No description available.

The development of swimming was investigated in zebrafish aged 1.5 to 5 days postfertilization by examining both the swimming behavior and its generation by the nervous system. Upon hatching (at day 2), swimming is undirected and occurs in sustained bursts of high frequency (mean = 67 Hz) tail undulations. By 4 days, the swimming pattern matures to a more directed, less erratic, beat-and-glide pattern where slower (mean = 35 Hz) tail undulations, lasting ∼200 ms, alternate with longer gliding rest periods. Swimming is powered by two classes of embryonic muscles (embryonic red, ER and white, EW) that are electrically coupled within (but not between) classes and have physiological properties similar to vertebrate tonic and twitch muscle, respectively. ER fibers have a lower chloride ion permeability than EW fibers and do not have sodium dependent action potentials. In paralyzed preparations, motoneurons and muscle fibers received coordinated excitatory synaptic activity (with left to right alternation and head to tail propagation) corresponding to either burst or beat-and-glide swimming. ER muscle was de-recruited at the fastest swimming rates and EW fibers dropped out at the slowest swimming rates. Rhythmic motoneuron output was generated by a phasic glutamatergic and a largely tonic glycinergic synaptic drive. Glutamatergic synapses had either or both AMPA/kainate and NMDA receptors and the kinetics of these synaptic currents were fixed throughout the developmental period examined. When depolarized, motoneurons fired high frequency (up to 800 Hz) bursts of action potentials that rapidly accommodated (within ∼20 ms) due to voltage and calcium dependent outwardly rectifying conductances. These intrinsic motoneuron properties are hypothesized to interact with the rhythmic synaptic drive to pattern motor output (at ∼25--75 Hz) to locomotor muscles. The neural generation of swimming in developing zebrafish is thus fundamentally similar to locomotion in adu

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No description available.

Locomotion automation is a very challenging and complex problem to solve. Besides the obvious navigation problems, there are also problems regarding the environment in which navigation has to be performed. Terrains with obstacles such as rocks, steps or high inclinations, among others, pose serious difficulties to normal wheeled vehicles. The flexibility of legged locomotion is ideal for these types of terrains but this alternate form of locomotion brings with it its own challenges to be solved, caused by the high number of degrees of freedom inherent to it. This problem is usually computationally intensive, so an alternative, using simple and hardware amenable bio-inspired systems, was studied. The goal of this thesis was to investigate if using a biologically inspired learning algorithm, integrated in a fully biologically inspired system, can improve its performance on irregular terrain by adapting its gait to deal with obstacles in its path. At first, two different versions of a learning algorithm based on unsupervised reinforcement learning were developed and evaluated. These systems worked by correlating different events and using them to adjust the behaviour of the system so that it predicts difficult situations and adapts to them beforehand. The difference between these versions was the implementation of a mechanism that allowed for some correlations to be forgotten and suppressed by stronger ones. Secondly, a depth from motion system was tested with unsatisfactory results. The source of the problems are analysed and discussed. An alternative system based on stereo vision was implemented, together with an obstacle detection system based on neuron and synaptic models. It is shown that this system is able to detect obstacles in the path of the robot. After the individual systems were completed, they were integrated together and the system performance was evaluated in a series of 3D simulations using various scenarios. These simulations allowed to conclude that both learning systems were able to adapt to simple scenarios but only the one capable of forgetting past correlations was able to adjust correctly in the more complex experiments.