Swimming in vertebrates such as eel and lamprey involves the coordination of alternating left and right activity in each segment. Forward swimming is achieved by a lag between the onset of activity in consecutive segments rostrocaudally along the spinal cord. The intersegmental phase lag is approximately 1% of the cycle duration per segment and is independent of the swimming frequency. Since the lamprey has approximately 100 spinal segments, at any given time one wave of activity is propagated along the body. Most previous simulations of intersegmental coordination in the lamprey have treated the cord as a chain of coupled oscillators or well-defined segments. Here a network model without segmental boundaries is described which can produce coordinated activity with a phase lag. This 'continuous' pattern-generating network is composed of a column of 420 excitatory interneurons (E1 to E420) and 300 inhibitory interneurons (C1 to C300) on each half of the simulated spinal cord. The interneurons are distributed evenly along the simulated spinal cord, and their connectivity is chosen to reflect the behavior of the intact animal and what is known about the length and strength of the synaptic connections. For example, E100 connects to all interneurons between E51 and E149, but at varying synaptic strengths, while E101 connects to all interneurons between E52 and E150. This unsegmented E-C network generates a motor pattern that is sampled by output elements similar to motoneurons (M cells), which are arranged along the cell column so that they receive input from seven E and five C interneurons. The M cells thus represent the summed excitatory and inhibitory input at different points along the simulated spinal cord and can be regarded as representing the ventral root output to the myotomes along the spinal cord. E and C interneurons have five simulated compartments and Hodgkin-Huxley based dynamics. The simulated network produces rhythmic output over a wide range of frequencies (1-11 Hz) with a phase lag constant over most of the length, with the exception of the 'cut' ends due to reduced synaptic input. As the inhibitory C interneurons in the simulation have more extensive caudal than rostral projections, the output of the simulation has positive phase lags, as occurs in forward swimming. However, unlike the biological network, phase lags in the simulation increase significantly with burst frequency, from 0.5% to 2.3% over the range of frequencies of the simulation. Local rostral or caudal increases in excitatory drive in the simulated network are sufficient to produce motor patterns with increased or decreased phase lags, respectively.
Most models of legged locomotion have concentrated on properties of either the mechanical or the neural system. Here a combined neuro-mechanical model of stepping in a single leg is presented, as the first step in the process of modeling and building a fast and dynamically stable quadruped. It is based on general principles of legged animals with special reference to vertebrates. The mechanical leg was first studied separately in order to take advantage of its inherent mechanical properties and avoid over-control during stepping generation. As a part of the design strategy it uses elastic actuators to increase shock tolerance and energy efficiency. The neural controller consists of a neural phase generator (NPG), a system of fast feedback pathways, and a single control neuron representing descending drive from higher centers in the brain. Sensory information directly influences the movements through the fast feedback pathways, but also entrains the NPG. The NPG has its own description of the state of the leg, which then enables it to set the feedback pathways so that only actions appropriate for the particular stage of the step cycle are undertaken. This preprogramming benefits from the NPG's ability to filter out any inconsistencies or gaps in the afferent input. In this way the model unites the use of central pattern generators and peripheral feedback systems for the generation of stepping movements. The neuro-mechanical system produced stable stepping patterns over a large velocity range and was adaptable to different body weights and landing from varying heights.
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