scholarly journals Timing precision in fly flight control: integrating mechanosensory input with muscle physiology

2020 ◽  
Vol 287 (1941) ◽  
pp. 20201774
Author(s):  
Bradley H. Dickerson
Keyword(s):  
Motor Neurons ◽  
Flight Control ◽  
Neural Circuit ◽  
Biomechanical Properties ◽  
Steering System ◽  
Spike Timing ◽  
Muscle Physiology ◽  
Mechanical Power Output ◽  
Essential Input ◽  
Length Changes

Animals rapidly collect and act on incoming information to navigate complex environments, making the precise timing of sensory feedback critical in the context of neural circuit function. Moreover, the timing of sensory input determines the biomechanical properties of muscles that undergo cyclic length changes, as during locomotion. Both of these issues come to a head in the case of flying insects, as these animals execute steering manoeuvres at timescales approaching the upper limits of performance for neuromechanical systems. Among insects, flies stand out as especially adept given their ability to execute manoeuvres that require sub-millisecond control of steering muscles. Although vision is critical, here I review the role of rapid, wingbeat-synchronous mechanosensory feedback from the wings and structures unique to flies, the halteres. The visual system and descending interneurons of the brain employ a spike rate coding scheme to relay commands to the wing steering system. By contrast, mechanosensory feedback operates at faster timescales and in the language of motor neurons, i.e. spike timing, allowing wing and haltere input to dynamically structure the output of the wing steering system. Although the halteres have been long known to provide essential input to the wing steering system as gyroscopic sensors, recent evidence suggests that the feedback from these vestigial hindwings is under active control. Thus, flies may accomplish manoeuvres through a conserved hindwing circuit, regulating the firing phase—and thus, the mechanical power output—of the wing steering muscles.

Aplysia ink release: central locus for selective sensitivity to long-duration stimuli

1979 ◽  
Vol 42 (5) ◽  
pp. 1223-1232 ◽  
Author(s):  
E. Shapiro ◽  
J. Koester ◽  
J. H. Byrne
Keyword(s):  
Motor Neuron ◽  
Spike Train ◽  
Motor Neurons ◽  
Neural Circuit ◽  
Reversal Potential ◽  
Secretory Process ◽  
Neuron Activity ◽  
Long Duration ◽  
Noxious Stimuli ◽  
Selective Sensitivity

1. A behavioral and electrophysiological analysis of defensive ink release in Aplysia californica was performed to examine the response of this behavior and its underlying neural circuit to various-duration noxious stimuli. 2. Three separate behavioral protocols were employed using electrical shocks to the head as noxious stimuli to elicit ink release. Ink release was found to be selectively responsive to longer duration stimuli, and to increase in a steeply graded fashion as duration is increased. 3. Intracellular stimulation of ink motor neurons revealed that ink release is a linear function of motor neuron spike train duration, indicating that the selective sensitivity of the behavior to long-duration stimuli is not due to a nonlinearity in the glandular secretory process. 4. In contrast, electrophysiological examination of ink motor neuron activity in response to sustained head shock revealed an accelerating spike train. During the later part of the spike train, compound excitatory synaptic potentials show a positive shift in reversal potential. 5. Our results suggest a central locus for the mechanisms that determine sensitivity of inking behavior to stimulus duration. 6. In contrast to ink release, defensive gill withdrawal was found to be extremely sensitive to short-duration stimuli.

scholarly journals Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

2019 ◽  
Author(s):  
Samantha Hughes ◽  
Tansu Celikel
Keyword(s):  
Motor Neurons ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Sensory Information ◽  
Circuit Complexity ◽  
Inhibitory Circuits ◽  
Invertebrate Species ◽  
Gating Mechanism ◽  
Motor Actions ◽  
Sensorimotor Representations

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

Mechanisms underlying nutrient-induced segmentation in isolated guinea pig small intestine

2007 ◽  
Vol 292 (4) ◽  
pp. G1162-G1172 ◽  
Author(s):  
R. M. Gwynne ◽  
J. C. Bornstein
Keyword(s):  
Small Intestine ◽  
Guinea Pig ◽  
Motor Neurons ◽  
Slow Wave ◽  
Neural Circuit ◽  
Circular Muscle ◽  
Decanoic Acid ◽  
Slow Waves ◽  
Longitudinal Position

Mechanisms underlying nutrient-induced segmentation within the gut are not well understood. We have shown that decanoic acid and some amino acids induce neurally dependent segmentation in guinea pig small intestine in vitro. This study examined the neural mechanisms underlying segmentation in the circular muscle and whether the timing of segmentation contractions also depends on slow waves. Decanoic acid (1 mM) was infused into the lumen of guinea pig duodenum and jejunum. Video imaging was used to monitor intestinal diameter as a function of both longitudinal position and time. Circular muscle electrical activity was recorded by using suction electrodes. Recordings from sites of segmenting contractions showed they are always associated with excitatory junction potentials leading to action potentials. Recordings from sites oral and anal to segmenting contractions revealed inhibitory junction potentials that were time locked to those contractions. Slow waves were never observed underlying segmenting contractions. In paralyzed preparations, intracellular recording revealed that slow-wave frequency was highly consistent at 19.5 (SD 1.4) cycles per minute (c/min) in duodenum and 16.6 (SD 1.1) c/min in jejunum. By contrast, the frequencies of segmenting contractions varied widely (duodenum: 3.6–28.8 c/min, median 10.8 c/min; jejunum: 3.0–27.0 c/min, median 7.8 c/min) and sometimes exceeded slow-wave frequencies for that region. Thus nutrient-induced segmentation contractions in guinea pig small intestine do not depend on slow-wave activity. Rather they result from a neural circuit producing rhythmic localized activity in excitatory motor neurons, while simultaneously activating surrounding inhibitory motor neurons.

scholarly journals Synergistic integration of Netrin and ephrin axon guidance signals by spinal motor neurons

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sebastian Poliak ◽  
Daniel Morales ◽  
Louis-Philippe Croteau ◽  
Dayana Krawchuk ◽  
Elena Palmesino ◽  
...  
Keyword(s):  
Motor Neurons ◽  
Molecular Mechanisms ◽  
Neural Circuit ◽  
Growth Cones ◽  
In Vitro Assays ◽  
Dependent Manner ◽  
Motor Axons ◽  
Spinal Motor Neurons ◽  
Spinal Motor

During neural circuit assembly, axonal growth cones are exposed to multiple guidance signals at trajectory choice points. While axonal responses to individual guidance cues have been extensively studied, less is known about responses to combination of signals and underlying molecular mechanisms. Here, we studied the convergence of signals directing trajectory selection of spinal motor axons entering the limb. We first demonstrate that Netrin-1 attracts and repels distinct motor axon populations, according to their expression of Netrin receptors. Quantitative in vitro assays demonstrate that motor axons synergistically integrate both attractive or repulsive Netrin-1 signals together with repulsive ephrin signals. Our investigations of the mechanism of ephrin-B2 and Netrin-1 integration demonstrate that the Netrin receptor Unc5c and the ephrin receptor EphB2 can form a complex in a ligand-dependent manner and that Netrin–ephrin synergistic growth cones responses involve the potentiation of Src family kinase signaling, a common effector of both pathways.

scholarly journals Where’s the noise? Key features of neuronal variability and inference emerge from self-organized learning

2014 ◽  
Author(s):  
Christoph Hartmann ◽  
Andreea Lazar ◽  
Jochen Triesch
Keyword(s):  
Spontaneous Activity ◽  
Network Model ◽  
Neural Circuit ◽  
Intrinsic Noise ◽  
Spike Timing ◽  
Homeostatic Plasticity ◽  
Neural Recordings ◽  
Self Organized ◽  
Key Features ◽  
Neural Variability

AbstractTrial-to-trial variability and spontaneous activity of cortical recordings have been suggested to reflect intrinsic noise. This view is currently challenged by mounting evidence for structure in these phenomena: Trial-to-trial variability decreases following stimulus onset and can be predicted by previous spontaneous activity. This spontaneous activity is similar in magnitude and structure to evoked activity and can predict decisions. Allof the observed neuronal properties described above can be accounted for, at an abstract computational level, by the sampling-hypothesis, according to which response variability reflects stimulus uncertainty. However, a mechanistic explanation at the level of neural circuit dynamics is still missing.In this study, we demonstrate that all of these phenomena can be accounted for by a noise-free self-organizing recurrent neural network model (SORN). It combines spike-timing dependent plasticity (STDP) and homeostatic mechanisms in a deterministic network of excitatory and inhibitory McCulloch-Pitts neurons. The network self-organizes to spatio-temporally varying input sequences.We find that the key properties of neural variability mentioned above develop in this model as the network learns to perform sampling-like inference. Importantly, the model shows high trial-to-trial variability although it is fully deterministic. This suggests that the trial-to-trial variability in neural recordings may not reflect intrinsic noise. Rather, it may reflect a deterministic approximation of sampling-like learning and inference. The simplicity of the model suggests that these correlates of the sampling theory are canonical properties of recurrent networks that learn with a combination of STDP and homeostatic plasticity mechanisms.Author SummaryNeural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary. In fact, the activity of a single neuron shows many features of a stochastic process. Furthermore, in the absence of a sensory stimulus, cortical spontaneous activity has a magnitude comparable to the activity observed during stimulus presentation. These findings have led to a widespread belief that neural activity is indeed very noisy. However, recent evidence indicates that individual neurons can operate very reliably and that the spontaneous activity in the brain is highly structured, suggesting that much of the noise may in fact be signal. One hypothesis regarding this putative signal is that it reflects a form of probabilistic inference through sampling. Here we show that the key features of neural variability can be accounted for in a completely deterministic network model through self-organization. As the network learns a model of its sensory inputs, the deterministic dynamics give rise to sampling-like inference. Our findings show that the notorious variability in neural recordings does not need to be seen as evidence for a noisy brain. Instead it may reflect sampling-like inference emerging from a self-organized learning process.

scholarly journals Release-dependent feedback inhibition by a presynaptically localized ligand-gated anion channel

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Seika Takayanagi-Kiya ◽  
Keming Zhou ◽  
Yishi Jin
Keyword(s):  
Motor Neurons ◽  
Neurotransmitter Release ◽  
Neural Circuit ◽  
Feedback Inhibition ◽  
Release Kinetics ◽  
Anion Channel ◽  
C Elegans ◽  
Release Of Acetylcholine ◽  
Presynaptic Release

Presynaptic ligand-gated ion channels (LGICs) have long been proposed to affect neurotransmitter release and to tune the neural circuit activity. However, the understanding of their in vivo physiological action remains limited, partly due to the complexity in channel types and scarcity of genetic models. Here we report that C. elegans LGC-46, a member of the Cys-loop acetylcholine (ACh)-gated chloride (ACC) channel family, localizes to presynaptic terminals of cholinergic motor neurons and regulates synaptic vesicle (SV) release kinetics upon evoked release of acetylcholine. Loss of lgc-46 prolongs evoked release, without altering spontaneous activity. Conversely, a gain-of-function mutation of lgc-46 shortens evoked release to reduce synaptic transmission. This inhibition of presynaptic release requires the anion selectivity of LGC-46, and can ameliorate cholinergic over-excitation in a C. elegans model of excitation-inhibition imbalance. These data demonstrate a novel mechanism of presynaptic negative feedback in which an anion-selective LGIC acts as an auto-receptor to inhibit SV release.

Neural Mechanism by Which Controlling Inputs Influence Motor Output in the Flying Locust

1967 ◽  
Vol 47 (2) ◽  
pp. 213-228
Author(s):  
INGRID WALDRON
Keyword(s):  
Motor Neuron ◽  
Motor Neurons ◽  
Neural Mechanism ◽  
Sensory Nerves ◽  
Rapid Variation ◽  
Flight Pattern ◽  
Burst Length ◽  
Rapid Changes ◽  
Length Changes ◽  
Individual Motor

1. The central nervous system of the flying locust generates a pattern of alternating bursts of impulses in the elevator and depressor motor neurons (Wilson, 1961). The mechanism by which controlling inputs modify this output pattern is analysed in this paper. 2. During roll turns and other flight manoeuvres the average number of impulses per burst (average burst length) changes in certain motor neurons. Changes in average burst length develop slowly, over tens of wingbeat cycles, even in response to the abrupt changes in input which result from electrical stimulation of sensory nerves. 3. In addition to the slow changes in average burst length which are elicited by controlling inputs, more rapid changes in burst length sometimes occur. During this rapid variation a longer burst is usually followed by a shorter burst, probably because the motor neuron is less excitable after a longer burst of activity. Burst length varies independently in different motor neurons. Both findings suggest that much of the rapid variation in burst length is due to changes occurring within the individual motor neurons, and is not a response to rapid changes in controlling inputs. 4. Under all conditions, changes in the number of impulses per burst are correlated with small changes in the relative timing of the burst; the longer bursts produced by a motor neuron begin slightly earlier in the wingbeat cycle. This implies that the factors which cause variation in the length of the bursts are also responsible for producing the variation in the timing of the bursts. 5. All of the observations can be explained on one assumption: that the only effect of controlling inputs is to cause slow changes in the ‘average excitation’ of individual motor neurons. Thus sensory and central control of the flight pattern generating system appears to be slow control over the average performance, rather than fast control over performance in a particular cycle.

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