A small-systems approach to motor pattern generation

Nusbaum, Michael P.; Beenhakker, Mark P.

doi:10.1038/417343a

Cited by 298 publications

(352 citation statements)

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“…Despite the importance of the input pattern, we show that the intrinsic properties of heart motoneurons play a substantive role in determining their output. While it is well established that motoneuron intrinsic properties contribute to motor pattern generation in the pyloric (Marder and Bucher 2007) and gastric mill (Nusbaum and Beenhakker 2002) networks of the crustacean stomatogastric system, almost all of the neurons within those CPGs are themselves motoneurons, and the stomatogastric system is not segmentally distributed like the leech heartbeat system. Because we used the identical segmental input patterns used in our canonical ensemble model, any differences between the hybrid system and model phasing should be attributable to the additional intrinsic properties of the living heart motoneurons not present in the model motoneurons.…”

Section: Discussionmentioning

confidence: 99%

“…Invertebrates, with their simple and accessible nervous systems, have long been useful for elucidating synaptic connectivity within central pattern generating networks (Nusbaum and Beenhakker 2002) and how motor patterns are modulated and selected (DeLong et al 2009). Here we use the leech heartbeat CPG to assess how motoneuron intrinsic properties and electrical coupling contribute to rhythmic motor output.…”

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confidence: 99%

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Contribution of motoneuron intrinsic properties to fictive motor pattern generation

Wright

Calabrese

2011

Journal of Neurophysiology

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Wright TM Jr, Calabrese RL. Contribution of motoneuron intrinsic properties to fictive motor pattern generation. J Neurophysiol 106: 538-553, 2011. First published May 11, 2011 doi:10.1152/jn.00101.2011.-Previously, we reported a canonical ensemble model of the heart motoneurons that underlie heartbeat in the medicinal leech. The model motoneurons contained a minimal set of electrical intrinsic properties and received a synaptic input pattern based on measurements performed in the living system. Although the model captured the synchronous and peristaltic motor patterns observed in the living system, it did not match quantitatively the motor output observed. Because the model motoneurons had minimal intrinsic electrical properties, the mismatch between model and living system suggests a role for additional intrinsic properties in generating the motor pattern. We used the dynamic clamp to test this hypothesis. We introduced the same segmental input pattern used in the model to motoneurons isolated pharmacologically from their endogenous input in the living system. We show that, although the segmental input pattern determines the segmental phasing differences observed in motoneurons, the intrinsic properties of the motoneurons play an important role in determining their phasing, particularly when receiving the synchronous input pattern. We then used trapezoidal input waveforms to show that the intrinsic properties present in the living system promote phase advances compared with our model motoneurons. Electrical coupling between heart motoneurons also plays a role in shaping motoneuron output by synchronizing the activity of the motoneurons within a segment. These experiments provide a direct assessment of how motoneuron intrinsic properties interact with their premotor pattern of synaptic drive to produce rhythmic output.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Contribution of motoneuron intrinsic properties to fictive motor pattern generation

Wright

Calabrese

2011

Journal of Neurophysiology

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“…The STNS, which consists of four ganglia plus their connecting and peripheral nerves, contains a set of central pattern-generating (CPG) circuits that control aspects of feeding (Nusbaum and Beenhakker, 2002). One of these ganglia, the stomatogastric ganglion (STG), contains two distinct but interacting rhythmically active circuits responsible for the chewing (gastric mill circuit) and filtering (pyloric circuit) of food (Harris-Warrick et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

Mechanosensory Activation of a Motor Circuit by Coactivation of Two Projection Neurons

Beenhakker¹,

Nusbaum²

2004

J. Neurosci.

102

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Individual neuronal circuits can generate multiple activity patterns because of the influence of different projection neurons. However, in most systems it has been difficult to identify and assess the relative contribution of all upstream neurons responsible for the activation of any single activity pattern by a behaviorally relevant stimulus. To elucidate this issue, we used the stomatogastric nervous system (STNS) of the crab. The STNS includes the gastric mill (chewing) motor circuit in the stomatogastric ganglion (STG) and no more than 20 projection neurons that innervate the STG. We previously identified at least some (four) of the projection neurons that are activated directly by the ventral cardiac neuron (VCN) system, a population of mechanosensory neurons that activates the gastric mill circuit. Here we show that two of these projection neurons, the previously identified modulatory commissural neuron 1 (MCN1) and commissural projection neuron 2 (CPN2), are necessary and likely sufficient for the initiation/maintenance of the VCN-elicited gastric mill rhythm. Selective inactivation of either MCN1 or CPN2 still enabled a VCN-elicited gastric mill rhythm. However, because MCN1 and CPN2 have different actions on gastric mill neurons, these manipulations resulted in rhythms distinct from each other and from that occurring in the intact system. After removal of both MCN1 and CPN2, VCN stimulation failed to activate the gastric mill rhythm. Selective conjoint stimulation of MCN1 and CPN2, approximating their VCN-elicited activity patterns and firing frequencies, elicited a VCN-like gastric mill rhythm. Thus the VCN mechanosensory system elicits the gastric mill rhythm via its activation of a subset of the relevant projection neurons.

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“…CPGs) to the execution of various rhythmic feeding behaviours. CPGs act in diverse neurobiological contexts such as flight, respiration, locomotion and chewing, and use a conserved set of principles that includes prevalence of synaptic inhibition and an ability to modify output firing patterns depending on the state of the organism [10]. The manner in which CPG organization is transduced into invertebrate feeding activities has been meticulously exposited for several behavioural models, including rhythmic movement of the toothed radula during feeding in the sea slug Aplysia [14] (Figure 1).…”

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confidence: 99%

“…Studies have often concentrated on small invertebrate circuits, leading to a wealth of knowledge about the neural basis for rhythmic behaviours such as respiration, locomotion and feeding. Systems used to model neural controls of feeding include the stomatogastric ganglion (STG) of the lobsters Panulirus and Homarus [9] and the crab Cancer [10], the buccal ganglia of the gastropods Aplysia and Lymnaea [11], and the subesophageal or frontal ganglia in insects such as the locust Locusta [12] or tobacco horn worm Manduca sexta [13]. These systems have been used to define how properties such as neuronal connections, neurotransmitter identities, membrane characteristics and inter-circuit communication contribute to specific feeding behaviours and to behavioural plasticity.…”

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confidence: 99%

The neurogenetics and evolution of food-related behaviour

Douglas

Dawson‐Scully

Sokolowski

2005

Trends in Neurosciences

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All organisms must acquire nutrients from the ambient environment to survive. In animals, the need to eat has driven the evolution of a rich array of complex foodrelated behaviours that ensure appropriate nutrient intake in diverse niches. Here, we review some of the neural and genetic components that contribute to the regulation of food-related behaviour in invertebrates, with emphasis on mechanisms that are conserved throughout various taxa and activities. We focus on synthesizing neurobiological and genetic approaches into a neurogenetic framework that explains foodrelated behaviour as the product of interactions between neural substrates, genes and internal and external environments.Introduction to food-related behaviours All life must acquire organic and inorganic substrates as fuel and raw material for energetic, metabolic and physiological processes. Whereas many plants synthesize their constituent organic compounds from carbon dioxide, water and sunlight, animals enact a heterotrophic strategy to gain necessary nutrients. In turn, they have evolved an impressive array of behaviours to detect, capture and ingest food. Broadly, food-related behaviour can be defined as actions elicited in response to hunger or the perception of food, therefore including food-searching activities, intake and post-ingestive behaviours. Multiple internal and external cues influence whether an animal is searching for food or eating at any given time. A major internal parameter is the level of hunger, which is governed by a milieu of regulatory factors interacting within an elaborate homeostatic system [1,2]. External factors include abundance and distribution of food, competitors, risk of predation, season and time of day. In humans, the decision to eat is complicated further by psychological and cultural factors. Owing to the breadth of contributing factors, food-related behaviour represents a rich interface that has been characterized from several different perspectives, including the ecological [3,4] A comprehensive description of any behaviour requires insight into the pattern and functioning of the neural circuits that underlie expression of the behaviour. Circuit output, which is often modifiable through experience, depends on parameters such as neuronal membrane

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A small-systems approach to motor pattern generation

Cited by 298 publications

References 93 publications

Contribution of motoneuron intrinsic properties to fictive motor pattern generation

Contribution of motoneuron intrinsic properties to fictive motor pattern generation

Mechanosensory Activation of a Motor Circuit by Coactivation of Two Projection Neurons

The neurogenetics and evolution of food-related behaviour

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