Scott L. Hooper

Central pattern generators

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Invertebrate muscles: thin and thick filament structure; molecular basis of contraction and its regulation, catch and asynchronous muscle.

This is the second in a series of canonical reviews on invertebrate muscle. We cover here thin and thick filament structure, the molecular basis of force generation and its regulation, and two special properties of some invertebrate muscle, catch and asynchronous muscle. Invertebrate thin filaments resemble vertebrate thin filaments, although helix structure and tropomyosin arrangement show small differences. Invertebrate thick filaments, alternatively, are very different from vertebrate striated thick filaments and show great variation within invertebrates. Part of this diversity stems from variation in paramyosin content, which is greatly increased in very large diameter invertebrate thick filaments. Other of it arises from relatively small changes in filament backbone structure, which results in filaments with grossly similar myosin head placements (rotating crowns of heads every 14.5 nm) but large changes in detail (distances between heads in azimuthal registration varying from three to thousands of crowns). The lever arm basis of force generation is common to both vertebrates and invertebrates, and in some invertebrates this process is understood on the near atomic level. Invertebrate actomyosin is both thin (tropomyosin:troponin) and thick (primarily via direct Ca(++) binding to myosin) filament regulated, and most invertebrate muscles are dually regulated. These mechanisms are well understood on the molecular level, but the behavioral utility of dual regulation is less so. The phosphorylation state of the thick filament associated giant protein, twitchin, has been recently shown to be the molecular basis of catch. The molecular basis of the stretch activation underlying asynchronous muscle activity, however, remains unresolved.

Crustacean Motor Pattern Generator Networks

Crustacean motor pattern-generating networks have played central roles in understanding the cellular and network bases of rhythmic motor patterns for over half a century. We review here the four best investigated of these systems: the stomatogastric, ventilatory, cardiac, and swimmeret systems. Generally applicable observations arising from this work include (1) neurons with active, endogenous cell properties (endogenous bursting, postinhibitory rebound, plateau potentials), (2) nonhierarchical (distributed) network synaptic connectivity patterns characterized by high levels of inter-neuronal connections, (3) nonspiking neurons and graded transmitter release, (4) multiple modulatory inputs, (5) networks that produce multiple patterns and have flexible boundaries, and (6) peripheral properties (proprioceptive feedback loops, low-frequency muscle filtering) playing an important role in motor pattern generation or expression.

Phase Maintenance in the Pyloric Pattern of the Lobster (Panulirus interruptus) Stomatogastric Ganglion

The extent to which individual neural networks can producephase-constant motor patterns as cycle frequency is altered has notbeen studied extensively. I investigated this issue in thewell-defined, rhythmic pyloric neural network. When pyloric cyclefrequency is altered three- to fivefold, pyloric inter-neuronaldelays shift by hundreds to thousands of msec, and all pyloricpattern elements show strong phase maintenance. The experimentalparadigm used is unlikely to activate exogenous inputs to thenetwork, and these delay changes are thus likely to arise fromphase-compensatory mechanisms intrinsic to the network. Pyloricinter-neuronal delays depend on the time constants of the network‘ssynapses and of the membrane properties of its neurons. The observeddelay shifts thus suggest that, in response to changes in overallcycle frequency, these constants vary so as to maintain patternphasing.

Sensory Feedback Induced by Front-Leg Stepping Entrains the Activity of Central Pattern Generators in Caudal Segments of the Stick Insect Walking System

Legged locomotion results from a combination of central pattern generating network (CPG) activity and intralimb and interlimb sensory feedback. Data on the neural basis of interlimb coordination are very limited. We investigated here the influence of stepping in one leg on the activities of neighboring-leg thorax–coxa (TC) joint CPGs in the stick insect (Carausius morosus). We used a new approach combining single-leg stepping with pharmacological activation of segmental CPGs, sensory stimulation, and additional stepping legs. Stepping of a single front leg could activate the ipsilateral mesothoracic TC CPG. Activation of the metathoracic TC CPG required that both ipsilateral front and middle legs were present and that one of these legs was stepping. Unlike the situation in real walking, ipsilateral mesothoracic and metathoracic TC CPGs activated by front-leg stepping fired in phase with the front-leg stepping. Local (intralimb) sensory feedback from load sensors could override this intersegmental influence of front-leg stepping, shifting retractor motoneuron activity relative to the front-leg step cycle and thereby uncoupling them from front-leg stepping. These data suggest that front-leg stepping in isolation would result in in-phase activity of all ipsilateral legs, and functional stepping gaits (in which the three ipsilateral legs do not step in synchrony) emerge because of local load sensory feedback overriding this in-phase influence.

Neural control of unloaded leg posture and of leg swing in stick insect, cockroach, and mouse differs from that in larger animals.

Stick insect (Carausius morosus) leg muscles contract and relax slowly. Control of stick insect leg posture and movement could therefore differ from that in animals with faster muscles. Consistent with this possibility, stick insect legs maintained constant posture without leg motor nerve activity when the animals were rotated in air. That unloaded leg posture was an intrinsic property of the legs was confirmed by showing that isolated legs had constant, gravity-independent postures. Muscle ablation experiments, experiments showing that leg muscle passive forces were large compared with gravitational forces, and experiments showing that, at the rest postures, agonist and antagonist muscles generated equal forces indicated that these postures depended in part on leg muscles. Leg muscle recordings showed that stick insect swing motor neurons fired throughout the entirety of swing. To test whether these results were specific to stick insect, we repeated some of these experiments in cockroach (Periplaneta americana) and mouse. Isolated cockroach legs also had gravity-independent rest positions and mouse swing motor neurons also fired throughout the entirety of swing. These data differ from those in human and horse but not cat. These size-dependent variations in whether legs have constant, gravity-independent postures, in whether swing motor neurons fire throughout the entirety of swing, and calculations of how quickly passive muscle force would slow limb movement as limb size varies suggest that these differences may be caused by scaling. Limb size may thus be as great a determinant as phylogenetic position of unloaded limb motor control strategy.

Transduction of temporal patterns by single neurons

As our ability to communicate by Morse code illustrates, nervous systems can produce motor outputs, and identify sensory inputs, based on temporal patterning alone. Although this ability is central to a wide range of sensory and motor tasks, the ways in which nervous systems represent temporal patterns are not well understood. I show here that individual neurons of the lobster pyloric network can integrate rhythmic patterned input over the long times (hundreds of milliseconds) characteristic of many behaviorally relevant patterns, and that their firing delays vary as a graded function of the patterns temporal character. These neurons directly transduce temporal patterns into a neural code, and constitute a novel biological substrate for temporal pattern detection and production. The combined activities of several such neurons can encode simple rhythmic patterns, and I provide a model illustrating how this could be achieved.

The Pyloric Pattern of the Lobster (Panulirus interruptus) Stomatogastric Ganglion Comprises Two Phase-Maintaining Subsets

The pyloric pattern approximately maintains phase over a three- tofivefold frequency range when the pattern is defined by the pacemakerburst beginning. However, in this reference frame certain patternelements maintain phase better than others, which suggestsphase-maintaining subgroups might exist. Reanalysis of these data inreference frames defined by each element shows the pattern containstwo groups of pattern elements within which phase is well maintainedbut between which maintenance is relatively poor. A third elementshows intermediate maintenance with each group. If ventriculardilator neuron burst beginning (VDB) is chosen as pattern beginning,all members of one group occur early in the pattern, all members ofthe other occur late in the pattern, and the intermediate elementoccurs between the groups. Thus, at least for phase maintenance, VDBis a “natural” pyloric pattern beginning. These results suggestfull description of complex patterns is best achieved by analysis inmany reference frames.

Motor neuron activity is often insufficient to predict motor response.

Our understanding of the necessity of considering peripheral properties when investigating how neural activity generates behavior has significantly increased in recent years. These advances include a theoretical analysis of the neuromuscular transform and a deeper understanding of the functional effects of non-linear contractile responses, slow muscle relaxation, and neuromodulation.

Central Pattern Generators

Central pattern generators (CPGs) are neural networks that can produce rhythmic patterned outputs without rhythmic sensory or central input. CPGs underlie the production of most rhythmic motor patterns and have been extensively studied as models of neural network function. Keywords: motor pattern; rhythmic movement; neural network; network oscillator; endogenous neuronal oscillator