Different Command Neurons Select Different Outputs from a Shared Premotor Interneuron of Crayfish Tail-Flip Circuitry

Kramer, Andrew; Krasne, Franklin B.; Bellman, Kirstie L.

doi:10.1126/science.7292013

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…They often buy the system time to plan and put into motion more sophisticated behaviours. The 10 msec tail flip in crayfish, which is among the fastest known animal reflexes, is soon overtaken by a slower response (50 msec) for directed swimming that is activated in parallel [2], [7] As shown by this example, a system may execute a rapid fixed response, foregoing assessment of the environment, reflection, planning, reasoning, and coordination in order to meet urgent time constraints. However, the system is also preparing a more reasoned and deliberate response that takes into account its previous exploration and knowledge of its operational environment.…”

Section: Description Of the Challengesmentioning

confidence: 99%

Interwoven Systems: Self-Improving Systems Integration

Bellman¹,

Tomforde

Würtz

2014

2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops

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Current trends in information and communication technology show that systems are increasingly influencing each other -which is seldom completely anticipated at design-time. As a result, mastering system integration with traditional methods becomes infeasible due to the resulting complexity. In this paper we argue that self-improving system integration is the most promising solution to counter the resulting challenges. Thereby, we highlight the different aspects of such a process with special attention to the optimisation question and discuss how approaches from the domain of self-organising systems -in particular Organic and Autonomic Computing -will be beneficial when researching possible solutions. I. MOTIVATIONInformation and communication technology (ICT) pervades every aspect of our daily lives. This inclusion changes our communities and all of our human interactions. It also presents a significant set of challenges in correctly designing and integrating our resulting technical systems. For instance, the embedding of ICT functionality in more and more devices (such as household appliances or a power grid's thermostats) leads to novel interconnections and a changing structure of the overall system. Not only technical systems are increasingly coupled, a variety of previously isolated natural and human systems have consolidated into a kind of overall system of systems -an interwoven system structure.In this context, the term "interwoven" [12] refers to the several aspects of coupling and mutual influences between component systems: heterogeneous elements interact, indirect influences of behaviour can be observed, and the context, cooperation and composition of systems are uncertain during runtime. For instance, legacy systems have to cooperate with novel solutions or different versions of the same system have to coexist and cooperate. This leads to novel challenges for system engineers and administrators.The ongoing integration of interwoven systems depends on the valid and timely knowledge of each of the participating systems on dynamically changing goals or priorities, on the state of its available resources and on the details of its operational context. It is infeasible to have such timely and intimate dynamically-changing knowledge of the participating systems come from an external master controller for the overall interwoven system. Hence, the most promising approach to master such systems relies on increasing their self-organisation capabilities. Within the last decade, initiatives like Autonomic Computing (AC) [6] and Organic Computing(OC) [11] have started to investigate and build systems based on the fundamentals of self-organisation: Concepts from natural organisms have been transferred to technical systems in order to achieve "life-like" characteristics such as adaptivity, robustness, and flexibility. Architectural patterns, customised machine learning techniques and collaboration mechanisms have been developed. However, these approaches and mechanisms have been developed with the focus on individual...

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Section: Description Of the Challengesmentioning

confidence: 99%

Interwoven Systems: Self-Improving Systems Integration

Bellman¹,

Tomforde

Würtz

2014

2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops

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“…In the giant fiber-mediated escape reaction, the command neurons (the giant fibers; GFs) directly connect to giant motoneurons (MoGs) specific of the GFmediated escape reaction, and to fast flexor motoneurons via a set of segmental giant interneurons (Kramer et al, 1981a;Kramer et al, 1981b). This design is likely the result of an evolutive adaptation that allows rapid responses (a few milliseconds).…”

Section: Example Of the Giant Tail Flip Programmentioning

confidence: 99%

Adaptive motor control in crayfish

Cattaert

Ray

2001

Progress in Neurobiology

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This article reviews the principles that rule the organization of motor commands that have been described over the past five decades in crayfish. The adaptation of motor behaviors requires the integration of sensory cues into the motor command. The respective roles of central neural networks and sensory feedback are presented in the order of increasing complexity. The simplest circuits described are those involved in the control of a single joint during posture (negative feedback-resistance reflex) and movement (modulation of sensory feedback and reversal of the reflex into an assistance reflex). More complex integration is required to solve problems of coordination of joint movements in a pluri-segmental appendage, and coordination of different limbs and different motor systems. In addition, beyond the question of mechanical fitting, the motor command must be appropriate to the behavioral context. Therefore, sensory information is used also to select adequate motor programs. A last aspect of adaptability concerns the possibility of neural networks to change their properties either temporarily (such on-line modulation exerted, for example, by presynaptic mechanisms) or more permanently (such as plastic changes that modify the synaptic efficacy). Finally, the question of how "automatic" local component networks are controlled by descending pathways, in order to achieve behaviors, is discussed.

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“…Because the effects of weakened or inhibited synapses can be overridden, at least in principle, it has gradually emerged that the giant axon motor circuits contain numerous mechanisms that might enable them to produce altered motor patterns to the same command (e.g. Kramer, Krasne & Bellman, 1981a;Miller et al 1984). Whether and under what circumstances they might do so is a question that is now being investigated.…”

Section: Selected Details Of the Neural Circuitry Involved In Giant-mediated Flexion Patternsmentioning

confidence: 99%

The Structural Basis of an Innate Behavioural Pattern

Wine

1984

Journal of Experimental Biology

193

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The abdominal nervous system of the crayfish contains six serially homologous ganglia, each containing approximately 650 neurones. No two ganglia are identical, and the ganglia interact extensively. Studies confined to intraganglionic interactions thus yield limited and sometimes misleading information. Each ganglion contains intrinsic (local) interneurones, motor neurones and projecting interneurones in roughly equal numbers, except in the specialized terminal ganglion where the ratio of these cells is approximately 3:2:1. Although the number of nerve cell bodies in a ganglion is small enough to be tractable, integration occurs in the neuropile, which contains terminals from interneurones and afferents that outnumber the neurones originating in the ganglion by at least ten to one. The abdominal nervous system responds almost exclusively to a variety of mechanosensory stimuli. It has very limited light sensitivity. Other modalities, notably chemosensitivity, are undescribed and may be lacking. The effectors of the abdomen consist of fast axial muscles (used for tailflip-powered escape), slow axial muscles (for setting abdominal posture), appendage muscles (for swimmeret beating), and slow muscles of the intestine and rectum (that control gut emptying). The fast and slow muscles of the tailfan are specialized homologues of the axial and appendage muscles. The abdominal nervous system represents only 3–4% of the 100 000 neurones within the crayfish central nervous system (CNS). Most sensory information gathered in the abdomen is transmitted to the rostral CNS for processing, and many abdominal motor programmes are activated by descending commands. Nevertheless, a surprising degree of autonomy is present, and at least some motor programmes of every motor system can be activated in isolated abdomens. Tailflip escape behaviour illustrates the integrative properties of the crayfish nervous system. Ninety pairs of efferents and eighteen pairs of interneurones have been identified within the abdominal portion of the escape circuit. A cell-by-cell analysis has so far provided neurophysiological explanations, in varying states of completeness, for ethological concepts such as innate releasing mechanisms, spatial patterning of movement, serial order in behaviour, and alterations in responsiveness to a constant stimulus.

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Different Command Neurons Select Different Outputs from a Shared Premotor Interneuron of Crayfish Tail-Flip Circuitry

Cited by 15 publications

References 18 publications

Interwoven Systems: Self-Improving Systems Integration

Interwoven Systems: Self-Improving Systems Integration

Adaptive motor control in crayfish

The Structural Basis of an Innate Behavioural Pattern

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