To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.

The appropriate growth of the neurons, accurate organization of their synapses, and successful neurotransmission are indispensable for sensorimotor activities. These processes are highly dynamic and tightly regulated. Extensive genetic, molecular, physiological, and behavioral studies have identified many molecular candidates and investigated their roles in various neuromuscular processes. In this article, we show that Beadex (Bx), the Drosophila LIM only (LMO) protein, is required for motor activities and neuromuscular growth of Drosophila. The larvae bearing Bx7, a null allele of Bx, and the RNAi-mediated neuronal-specific knockdown of Bx show drastically reduced crawling behavior, a diminished synaptic span of the neuromuscular junctions (NMJs) and an increased spontaneous neuronal firing with altered motor patterns in the central pattern generators (CPGs). Microarray studies identified multiple targets of Beadex that are involved in different cellular and molecular pathways, including those associated with the cytoskeleton and mitochondria that could be responsible for the observed neuromuscular defects. With genetic interaction studies, we further show that Highwire (Hiw), a negative regulator of synaptic growth at the NMJs, negatively regulates Bx, as the latter\'s deficiency was able to rescue the phenotype of the Hiw null mutant, HiwDN. Thus, our data indicate that Beadex functions downstream of Hiw to regulate the larval synaptic growth and physiology.NEW & NOTEWORTHY A novel role for Beadex (Bx) regulates the larval neuromuscular junction (NMJ) structure and function in a tissue-specific manner. Bx is expressed in a subset of Toll-6-expressing neurons and is involved in regulating synaptic span and physiology, possibly through its negative interaction with Highwire (Hiw). The findings of this study provide insights into the molecular mechanisms underlying NMJ development and function and warrant further investigation to understand the role of Bx in these processes fully.

BACKGROUND: The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system.
METHODS: In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli.
CONCLUSIONS: These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.

In response to a suitably aversive skin stimulus, the marine mollusk Tritonia diomedea launches an escape swim followed by several minutes of high-speed crawling. The two escape behaviors are highly dissimilar: whereas the swim is a muscular behavior involving alternating ventral and dorsal whole body flexions, the crawl is a nonrhythmic gliding behavior mediated by the beating of foot cilia. The serotonergic dorsal swim interneurons (DSIs) are members of the swim central pattern generator (CPG) and also strongly drive crawling. Although the swim network is very well understood, the Tritonia crawling network to date comprises only three neurons: the DSIs and pedal neurons 5 and 21 (Pd5 and Pd21). Since Tritonia\'s swim network has been suggested to have arisen from a preexisting crawling network, we examined the possible role that another swim CPG neuron, C2, may play in crawling. Because of its complete silence in the postswim crawling period, C2 had not previously been considered to play a role in driving crawling. However, semi-intact preparation experiments demonstrated that a brief C2 spike train surprisingly and strongly drives the foot cilia for ∼30 s, something that cannot be explained by its synaptic connections to Pd5 and Pd21. Voltage-sensitive dye (VSD) imaging in the pedal ganglion identified many candidate crawling motor neurons that fire at an elevated rate after the swim and also revealed several pedal neurons that are strongly excited by C2. It is intriguing that unlike the DSIs, which fire tonically after the swim to drive crawling, C2 does so despite its postswim silence.NEW & NOTEWORTHY Tritonia swim central pattern generator (CPG) neuron C2 surprisingly and strongly drives the early phase of postswim crawling despite being silent during this period. In decades of research, C2 had not been suspected of driving crawling because of its complete silence after the swim. Voltage-sensitive dye imaging revealed that the Tritonia crawling motor network may be much larger than previously known and also revealed that many candidate crawling neurons are excited by C2.

Swallowing is induced by a central pattern generator in the nucleus tractus solitarius (NTS). We aimed to create a medullary slice preparation to elucidate the neural architecture of the central pattern generator of swallowing (Sw-CPG) and record its neural activities. Experiments were conducted on 2-day-old Sprague-Dawley rats (n = 46). The brainstem-spinal cord was transected at the pontomedullary and cervicothoracic junctions; the medulla was sliced transversely at thicknesses of 600, 700, or 800 μm. The rostral end of the slice was 100 μm rostral to the vagus nerve. We recorded hypoglossal nerve activity and electrically stimulated the vagus nerve or microinjected bicuculline methiodide (BIC) into the NTS. The 800-μm slices generated both rhythmic respiratory activity and electrically elicited neural activity. The 700-μm slices generated only respiratory activity, while the 600-μm slices did not generate any neural activity. BIC microinjection into the NTS in 800-μm slices resulted in the typical activity that closely resembled the swallowing activity reported in other experiments. This swallowing-like activity consistently lengthened the respiratory interval. Despite complete inhibition of respiratory activity, weak swallowing-like activity was observed under bath application of a non-NMDA receptor antagonist. Contrastingly, bath application of NMDA receptor antagonists resulted in a complete loss of swallowing-like activity and no change in respiratory activity. These results suggest that the 800-μm medullary slice preparation contains both afferent and efferent neural circuits and pattern generators of swallowing activity. Additionally, NMDA receptors may be necessary for generating swallowing activity. This medullary slice preparation can therefore elucidate Sw-CPG neural networks.

Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses.

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Circadian rhythms in physiology and behavior sync organisms to external environmental cycles. Here, circadian oscillation in intracellular chloride in central pacemaker neurons of the fly, Drosophila melanogaster, is reviewed. Intracellular chloride links SLC12 cation-coupled chloride transporter function with kinase signaling and the regulation of inwardly rectifying potassium channels.

Central pattern generators are small networks that contribute to generating animal locomotion. The models used to study gait generation and gait transition mechanisms often require biologically accurate neuron and synapse models, with high dimensionality and complex dynamics. Tuning the parameters of these models to elicit network dynamics compatible with gait features is not a trivial task, due to the impossibility of inferring a priori the effects of each parameter on the nonlinear system\'s emergent dynamics. In this paper we explore the use of global optimization strategies for parameter optimization in multigait central pattern generator (CPG) models with complex cell dynamics and minimal topology. We first consider an existing quadruped CPG model as a test bed for the objective function formulation, then proceed to optimize the parameters of a newly proposed multigait, interlimb hexapod CPG model. We successfully obtain hexapod gaits and prompt gait transitions by varying only control currents, while all CPG parameters, once optimized, are kept fixed. This mechanism of gait transitions is compatible with short-term synaptic plasticity.

The most prominent symptom of Alzheimer\'s disease (AD) is cognitive decline; however, sleep and other circadian disruptions are also common in AD patients. Sleep disruptions have been connected with memory problems and therefore the changes in sleep patterns observed in AD patients may also actively contribute to cognitive decline. However, the underlying molecular mechanisms that connect sleep disruptions and AD are unclear. A characteristic feature of AD is the formation of plaques consisting of Amyloid-β (Aβ) peptides generated by cleavage of the Amyloid Precursor Protein (APP). Besides Aβ, APP cleavage generates several other fragments, including the APP intracellular domain (AICD) that has been linked to transcriptional regulation and neuronal homeostasis. Here we show that overexpression of the AICD reduces the early evening expression of two core clock genes and disrupts the sleep pattern in flies. Analyzing the subcellular localization of the AICD in pacemaker neurons, we found that the AICD levels in the nucleus are low during daytime but increase at night. While this pattern of nuclear AICD persisted with age, the nighttime levels were higher in aged flies. Increasing the cleavage of the fly APP protein also disrupted AICD nuclear localization. Lastly, we show that the day/nighttime nuclear pattern of the AICD is also detectable in neurons in the suprachiasmatic nucleus of mice and that it also changes with age. Together, these data suggest that AD-associated changes in APP processing and the subsequent changes in AICD levels may cause sleep disruptions in AD.