Reciprocal inhibition: A mechanism underlying oscillatory animal movements

Wo, Friesen

doi:10.1016/0149-7634(94)90010-8

Cited by 122 publications

(78 citation statements)

References 13 publications

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“…One of the strongest criteria one can use to determine whether a particular neuron is a member of the CPG for a rhythmic behavior is a demonstration that the phase of the behavior can be shifted in a predictable manner by altering the firing pattern in the neuron (Friesen, 1994). We designed an experiment to test this as follows.…”

Section: Stimulation Of Sint1 Shifts the Phase Of Swimmingmentioning

confidence: 99%

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Central pattern generator for swimming in Melibe

Thompson

Watson

2005

Journal of Experimental Biology

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SUMMARY The nudibranch mollusc Melibe leonina swims by bending from side to side. We have identified a network of neurons that appears to constitute the central pattern generator (CPG) for this locomotor behavior, one of only a few such networks to be described in cellular detail. The network consists of two pairs of interneurons, termed `swim interneuron 1' (sint1) and`swim interneuron 2' (sint2), arranged around a plane of bilateral symmetry. Interneurons on one side of the brain, which includes the paired cerebral, pleural and pedal ganglia, coordinate bending movements toward the same side and communicate via non-rectifying electrical synapses. Interneurons on opposite sides of the brain coordinate antagonistic movements and communicate over mutually inhibitory synaptic pathways. Several criteria were used to identify members of the swim CPG, the most important being the ability to shift the phase of swimming behavior in a quantitative fashion by briefly altering the firing pattern of an individual neuron. Strong depolarization of any of the interneurons produces an ipsilateral swimming movement during which the several components of the motor act occur in sequence. Strong hyperpolarization causes swimming to stop and leaves the animal contracted to the opposite side for the duration of the hyperpolarization. The four swim interneurons make appropriate synaptic connections with motoneurons, exciting synergists and inhibiting antagonists. Finally, these are the only neurons that were found to have this set of properties in spite of concerted efforts to sample widely in the Melibe CNS. This led us to conclude that these four cells constitute the CPG for swimming. While sint1 and sint2 work together during swimming, they play different roles in the generation of other behaviors. Sint1 is normally silent when the animal is crawling on a surface but it depolarizes and begins to fire in strong bursts once the foot is dislodged and the animal begins to swim. Sint2 also fires in bursts during swimming, but it is not silent in non-swimming animals. Instead activity in sint2 is correlated with turning movements as the animal crawls on a surface. This suggests that the Melibe motor system is organized in a hierarchy and that the alternating movements characteristic of swimming emerge when activity in sint1 and sint2 is bound together.

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Section: Stimulation Of Sint1 Shifts the Phase Of Swimmingmentioning

confidence: 99%

“…While this concept is widely accepted (Friesen, 1994), few examples of locomotor CPG networks are known in detail. The nudibranch mollusc Melibe leonina swims by bending from side to side in a behavior that can continue for hours in freely swimming animals (Hurst, 1968;Watson et al, 2001Watson et al, , 2002Lawrence and Watson, 2002).…”

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confidence: 99%

Central pattern generator for swimming in Melibe

Thompson

Watson

2005

Journal of Experimental Biology

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“…At present, detailed knowledge exists on the generation of a basic locomotor output for a variety of locomotor behaviours, such as swimming (e.g. Friesen 1994;Arshavsky et al 1998;Grillner 2003), walking (Bässler & Büschges 1998;Pearson & Gordon 2000) and flying (Robertson 2003). It is well established that the generation of locomotor patterns results from a close interaction between central pattern-generating (CPG) networks in the nervous system, local feedback from sensory neurons about movements and forces generated in the locomotor organs, and coordinating signals from neighbouring segments or appendages (Pearson 1995;Grillner 2003;Büschges 2005).…”

Section: Introductionmentioning

confidence: 99%

Control of stepping velocity in a single insect leg during walking

Gabriel

Büschges

2006

Phil. Trans. R. Soc. A.

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In the single middle leg preparation of the stick insect walking on a treadmill, the activity of flexor and extensor tibiae motor neurons and muscles, which are responsible for the movement of the tibia in stance and swing phases, respectively, was investigated with respect to changes in stepping velocity. Changes in stepping velocity were correlated with cycle period. There was a close correlation of flexor motor neuron activity (stance phase) with stepping velocity, but the duration and activation of extensor motor neurons (swing phase) was not altered. The depolarization of flexor motor neurons showed two components. At all step velocities, a stereotypic initial depolarization was generated at the beginning of stance phase activity. A subsequent larger depolarization and activation was tightly linked to belt velocity, i.e. it occurred earlier and with larger amplitude during fast steps compared with slow steps. Alterations in a tonic background excitation appear not to play a role in controlling the motor neuron activity for changes in stepping velocity. Our results indicate that in the single insect leg during walking, mechanisms for altering stepping velocity become effective only during an already ongoing stance phase motor output. We discuss the putative mechanisms involved.

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“…These two cells (PD and LP) have inhibitory synapses on one another. In normal circumstances, the two cells burst in antiphase [8,28,30,33,34]. However, when the LP cell is given constant hyperpolarizing The PD and LP neurons mutually inhibit each other; each neuron's membrane is being monitored intracellularly (V electrodes), and current can be injected into the LP through a second electrode (i electrode).…”

Section: Motivating Examplementioning

confidence: 99%

Timing regulation in a network reduced from voltage-gated equations to a one-dimensional map

LoFaro

Kopell

1999

Journal of Mathematical Biology

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Abstract. We discuss a method by which the dynamics of a network of neurons, coupled by mutual inhibition, can be reduced to a onedimensional map. This network consists of a pair of neurons, one of which is an endogenous burster, and the other excitable but not bursting in the absence of phasic input. The latter cell has more than one slow process. The reduction uses the standard separation of slow/fast processes; it also uses information about how the dynamics on the slow manifold evolve after a "nite amount of slow time. From this reduction we obtain a one-dimensional map dependent on the parameters of the original biophysical equations. In some parameter regimes, one can deduce that the original equations have solutions in which the active phase of the originally excitable cell is constant from burst to burst, while in other parameter regimes it is not. The existence or absence of this kind of regulation corresponds to qualitatively di!erent dynamics in the one-dimensional map. The computations associated with the reduction and the analysis of the dynamics includes the use of coordinates that parameterize by time along trajectories, and &&singular PoincareH maps'' that combine information about #ows along a slow manifold with information about jumps between branches of the slow manifold.

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Reciprocal inhibition: A mechanism underlying oscillatory animal movements

Cited by 122 publications

References 13 publications

Central pattern generator for swimming in Melibe

Central pattern generator for swimming in Melibe

Control of stepping velocity in a single insect leg during walking

Timing regulation in a network reduced from voltage-gated equations to a one-dimensional map

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