Developmental switch in the function of inhibitory commissural V0d interneurons in zebrafish

Picton, Laurence D.; Björnfors, E. Rebecka; Fontanel, Pierre; Pallucchi, Irene; Bertuzzi, Maria; Manira, Abdeljabbar El

doi:10.1016/j.cub.2022.06.059

Cited by 8 publications

(5 citation statements)

References 75 publications

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“…In contrast, in zebrafish, the V0, V1, and V2 classes are largely homogeneously distributed in the rostrocaudal axis (Björnfors & El Manira 2016, Callahan et al 2019, Kimura et al 2006, Menelaou et al 2014, Picton et al 2022. These results may suggest that segmental variations are more relevant to the functional demands of limb control, or they may reflect the larger locomotor repertoire of mice compared to fish.…”

Section: Genetic and Cellular Variation In The Longitudinal Axismentioning

confidence: 84%

“…Motor neurons are born in a stereotyped sequence, with primary motor neurons that innervate fast muscle being born first, and secondary motor neurons innervating slow muscle being born later. This pattern of early-born neurons for fast motor control and later-born neurons for slow motor control appears to extend to V1, V2a, and V0d neurons , McLean & Fetcho 2009, Picton et al 2022, Satou et al 2020 as well as to descending circuitry (Liu et al 2022, Pujala & Koyama 2019. Intrinsic physiology and morphology of motor and premotor populations exhibit birthdate-related gradients as well: Early-born V2a neurons typically have longer axons and lower input resistance than do late-born neurons (McLean & Fetcho 2009, Menelaou et al 2014).…”

Section: Neuronal Birthdate As Diversifying Factormentioning

confidence: 89%

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Spinal Interneurons: Diversity and Connectivity in Motor Control

Sengupta

Bagnall

2023

Annu. Rev. Neurosci.

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The spinal cord is home to the intrinsic networks for locomotion. An animal in which the spinal cord has been fully severed from the brain can still produce rhythmic, patterned locomotor movements as long as some excitatory drive is provided, such as physical, pharmacological, or electrical stimuli. Yet it remains a challenge to define the underlying circuitry that produces these movements because the spinal cord contains a wide variety of neuron classes whose patterns of interconnectivity are still poorly understood. Computational models of locomotion accordingly rely on untested assumptions about spinal neuron network element identity and connectivity. In this review, we consider the classes of spinal neurons, their interconnectivity, and the significance of their circuit connections along the long axis of the spinal cord. We suggest several lines of analysis to move toward a definitive understanding of the spinal network. Expected final online publication date for the Annual Review of Neuroscience, Volume 46 is July 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Genetic and Cellular Variation In The Longitudinal Axismentioning

confidence: 84%

Section: Neuronal Birthdate As Diversifying Factormentioning

confidence: 89%

Spinal Interneurons: Diversity and Connectivity in Motor Control

Sengupta

Bagnall

2023

Annu. Rev. Neurosci.

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“… 2022 ; Picton et al. 2022 ). Recent spinal cord models, that also include learning, had success in explaining how prominent connectivity patterns among spinal neurons and afferents could form (Enander et al.…”

Section: Discussionmentioning

confidence: 99%

“…One genetically defined class of spinal interneuron is known to change its role in locomotor control during development from larvae to adult (Picton et al. 2022 ). Moreover, afferent wiring is activity dependent (Granmo et al.…”

Section: Introductionmentioning

confidence: 99%

The Bcm rule allows a spinal cord model to learn rhythmic movements

Kohler,

Röhrbein,

Knoll

et al. 2023

Biol Cybern

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Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock–Cooper–Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.

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“…Another theory explaining speed-dependent changes in coordination stratifies ‘rhythm’ and ‘pattern’ control (Perret and Cabelguen, 1980; Koshland and Smith, 1989; Kriellaars et al, 1994; Burke et al, 2001; Lafreniere-Roula and McCrea, 2005), where populations of interneurons generating higher or lower frequency rhythms feed into separate populations coordinating distinct patterns of motor output (McCrea and Rybak, 2008; Ausborn et al, 2019). In support, studies in zebrafish and mice suggest separate spinal populations are active at different locomotor speeds with distinct patterns of motor output (McLean et al, 2008; Talpalar et al, 2013; Satou et al, 2020; Picton et al, 2022). Critically, however, rhythmogenesis is presumed to originate from a common mechanism regardless of speed, reliant on cellular properties or synaptic drive or both (Guertin, 2009; Brocard et al, 2010; Ryczko et al, 2010; Ziskind-Conhaim and Hochman, 2017; El Manira, 2023).…”

Section: Introductionmentioning

confidence: 91%

Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish

Agha,

Kishore,

McLean

2024

Preprint

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Different speeds of locomotion require heterogeneous spinal populations, but a common mode of rhythm generation is presumed to exist. Here, we explore the cellular versus synaptic origins of spinal rhythmicity at different speeds by performing electrophysiological recordings from premotor excitatory interneurons in larval zebrafish. Chx10-labeled V2a neurons are divided into at least two subtypes proposed to play distinct roles in timing and intensity control. Consistent with distinct rhythm generating and output patterning functions within the spinal V2a population, we find that one subtype is recruited exclusively at slow or fast speeds and exhibits intrinsic cellular properties suitable for rhythmogenesis at those speeds, while the other subtype is recruited more reliably at all speeds and lacks appropriate rhythmogenic cellular properties. Unexpectedly, however, phasic firing patterns during locomotion in rhythmogenic and non-rhythmogenic subtypes are best explained by distinct modes of synaptic inhibition linked to cell-type and speed. At fast speeds reciprocal inhibition in rhythmogenic V2a neurons supports phasic firing, while recurrent inhibition in non-rhythmogenic V2a neurons helps pattern motor output. In contrast, at slow speeds recurrent inhibition in rhythmogenic V2a neurons supports phasic firing, while non-rhythmogenic V2a neurons rely on reciprocal inhibition alone to pattern output. Our findings suggest cell-type-specific, not common, modes of rhythmogenesis generate and coordinate different speeds of locomotion.

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Developmental switch in the function of inhibitory commissural V0d interneurons in zebrafish

Cited by 8 publications

References 75 publications

Spinal Interneurons: Diversity and Connectivity in Motor Control

Spinal Interneurons: Diversity and Connectivity in Motor Control

The Bcm rule allows a spinal cord model to learn rhythmic movements

Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish

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