Pattern Generator Research Articles

SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach

The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks.

We present a novel set of quantitative measures for "likeness" (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative "blended" system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron's rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

A Bioinspired Control Strategy Ensures Maneuverability and Adaptability for Dynamic Environments in an Underactuated Robotic Fish

Bioinspired underwater robots can move efficiently, with agility, even in complex aquatic areas, reducing marine ecosystem disturbance during exploration and inspection. These robots can improve animal farming conditions and preserve wildlife. This study proposes a muscle-like control for an underactuated robot in carangiform swimming mode. The artifact exploits a single DC motor with a non-blocking transmission system to convert the motor’s oscillatory motion into the fishtail’s oscillation. The transmission system combines a magnetic coupling and a wire-driven mechanism. The control strategy was inspired by central pattern generators (CPGs) to control the torque exerted on the fishtail. It integrates proprioceptive sensory feedback to investigate the adaptability to different contexts. A parametrized control law relates the reference target to the fishtail’s angular position. Several tests were carried out to validate the control strategy. The proprioceptive feedback revealed that the controller can adapt to different environments and tail structure changes. The control law parameters variation accesses the robotic fish’s multi-modal swimming. Our solution can vary the swimming speed of 0.08 body lengths per second (BL/s), and change the steering direction and performance by an angular speed and turning curvature radius of 0.08 rad/s and 0.25 m, respectively. Performance can be improved with design changes, while still maintaining the developed control strategy. This approach ensures the robot’s maneuverability despite its underactuated structure. Energy consumption was evaluated under the robotic platform’s control and design. Our bioinspired control system offers an effective, reliable, and sustainable solution for exploring and monitoring aquatic environments, while minimizing human risks and preserving the ecosystem. Additionally, it creates new and innovative opportunities for interacting with marine species. Our findings demonstrate the potential of bioinspired technologies to advance the field of marine science and conservation.

Reliable intracavity reflection for self-injection locking lasers and microcomb generation

Self-injection locking has emerged as a crucial technique for coherent optical sources, spanning from narrow linewidth lasers to the generation of localized microcombs. This technique involves key components, namely a laser diode and a high-quality cavity that induces narrow-band reflection back into the laser diode. However, in prior studies, the reflection mainly relied on the random intracavity Rayleigh backscattering, rendering it unpredictable and unsuitable for large-scale production and wide-band operation. In this work, we present a simple approach to achieve reliable intracavity reflection for self-injection locking to address this challenge by introducing a Sagnac loop into the cavity. This method guarantees robust reflection for every resonance within a wide operational band without compromising the quality factor or adding complexity to the fabrication process. As a proof of concept, we showcase the robust generation of narrow linewidth lasers and localized microcombs locked to different resonances within a normal-dispersion microcavity. Furthermore, the existence and generation of localized patterns in a normal-dispersion cavity with broadband forward–backward field coupling is first proved, as far as we know, both in simulation and in experiment. Our research offers a transformative approach to self-injection locking and holds great potential for large-scale production.

Lumbar epidural electrical stimulation enhances respiration in mice

Spinal cord epidural electrical stimulation (EES) has emerged as a novel approach to modulate and interrogate the neural circuit(s) underlying sensorimotor functions in the past few decades. Recent studies have revealed that cervical EES can regulate respiration in both human subjects and animal models. Furthermore, coupled cervical and lumbar stimulations significantly increased the rhythmic motor outputs in both the cervical and lumbar spinal cord. We hypothesize that the lumbar and cervical coupling is underpinned by cervico-lumbar synaptic connections, and thus, EES at level of the lumbar spinal cord alone may also facilitate respiration via the cervico-lumbar circuit. In this study, we applied EES to the dorsal surface of the lumbar spinal cord between levels 1 and 6 (L1-6) in anesthetized mice. We found that rostral lumbar (L1-2) EES at a stimulation intensity of 0.5 mA elicited significantly higher respiratory frequency, tidal volume, and minute ventilation responses compared to the other lumbar levels (L3-4 and L5-6) tested at intensities ranging from 0.1-1.0 mA. The increased respiratory activity elicited by lumbar stimulation persisted even after the stimulation ceased. Using the optimal stimulation parameters that we identified, we further investigated the underlying mechanism of the lumbar EES-induced respiratory response by examining the neuronal activation patterns. Using immunostaining, we detected c-fos signals, a marker of neuronal activity, in the cervical spinal cord and brainstem after EES at L1-2 using 0.5 mA at 30Hz. This indicates that focal lumbar stimulation elicits remote neuronal activation in cervical spinal cord and brainstem, regions that contain respiration-related neural circuits. These findings suggest that lumbar EES may facilitate respiration by recruiting paraspinal respiratory circuits and potentially supra-spinal respiratory central pattern generator(s). This study provides insights into the development of novel approaches to restore normal respiratory patterns for patients with respiratory disorders, particularly spinal cord injuries. This work was supported by The Louis and Harold Price Foundation, H & H Evergreen Foundation, Jonathan and Susan Dolgen Foundation, the National Institute of Drug Abuse (R01DA047637), and the National Institute of Neurological Disorders and Stroke (UH3NS119772) in the National Institutes of Health. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Respiration and Cough in Pompe Disease: Results from a neuromechanical computational model of the respiratory control system

Late Onset Pompe Disease (LOPD) is an incurable, rare progressive genetic disease. It is characterized by glycogen build-up, due to mutations in the gene encoding the lysosomal enzyme acid alpha glucosidase, throughout muscle and neural regions. This accumulation is associated with cell death and muscle fiber swelling, eventually leading to respiratory insuffciency and airway clearance (i.e. cough) deficiency. Impairments in effective airway clearance result from the inability to clear normal pulmonary volume. Emerging evidence indicates significant respiratory neuron dysfunction, altering pulmonary function in humans and animal models. It has been suggested that respiratory and airway clearance insuffciency observed in LOPD may be attributed to medullary motoneurons potentially associated with the respiratory and cough pattern generators (Fuller et al., 2013; DeRuisseau et al., 2009). However, the precise neural mechanisms that suppress breathing and cough are not fully understood. One possible target for LOPD respiratory insuffciencies may be reduced medullary inspiratory neuron activity within this network, which could result in decreased phrenic motoneuron output. Using a stochastic neural network simulator that consisted of discrete “integrate and fire” populations, we simulated a systematic decrease in three medullary inspiratory neuron populations: inspiratory driver (I-Driver), inspiratory decrementing (I-Dec), or inspiratory augmenting (I-Aug) neurons. We simulated breathing and three cough trials. By targeting these populations, we expected global decreased neural bursts, suppressed inspiratory motor drive, and/or suppressed expiratory motoneuron output. Simulations indicated that when the I-Dec motoneuron population decreased by 25%, it resulted in apnea and abolished cough. Reducing the I-Aug motoneuron population by 20% resulted in longer inspiratory phase durations (TI) and a decreased respiratory rate. During cough, there was a decrease in the duration and amplitudes of the expiratory motoneuronal activity associated with decreased cough receptor and pulmonary stretch receptor afferent feedback. When the I-Driver motoneuron population was reduced by 25%, TI decreased and I amplitude increased during respiration. During cough, lumbar and phrenic motoneuron discharge duration and amplitude decreased. These results are consistent with previously reported in vivo data. Overall, our simulation data support the plausibility of glycogen storage dysfunction impairing medullary neuronal and premotoneuronal excitability within the inspiratory network. Our results also suggest that some neurons may be more sensitive to glycogen storage than others within the brainstem inspiratory network. Supported by NHLBI L30HL165496, NIH T32 HL 134621 and 3OT2OD023854. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Back Stimulation with Gaussian Frequency Biphasic Burst Stimulation for Swallowing Motor Activation

Aspiration of food containing bacteria or saliva into the trachea, leading to bacterial lung infection, is termed aspiration pneumonia. In the United States, aspiration pneumonia results in over 57,000 deaths annually, constituting 2.3% of the total mortality. Similarly, according to data from 2021 in Japan, more than 49,000 individuals succumbed to aspiration pneumonia, accounting for approximately 3.4% of the total mortality, making it the sixth leading cause of death in Japan. Notably, the risk of mortality due to aspiration pneumonia can be mitigated through early detection and prevention of swallowing disorders. Consequently, this research focuses on rehabilitation strategies for swallowing disorders. Swallowing is a coordinated motor function, orchestrated by a program involving multiple muscles under the directives of the Swallow Central Pattern Generator (CPG) located in the medulla. Thus, activation of the Swallowing CPG is pivotal for effective swallowing rehabilitation. To this end, we propose a novel method to activate the Swallow CPG by electrical stimulating of the back. The back has been recognized as a site that activates the expiratory phase of respiration, where the swallow reflex is often triggered. Moreover, given the adjacency of the Swallowing CPG and the Respiratory CPG, we postulate that stimulating the back to activate the expiratory phase can lead to enhanced activation of swallowing movements. Methods: Surface electrodes were placed on the back (T9~T10) and neck of adult rats. Initially, burst stimuli of two polarities were randomly generated at frequencies of either 30 Hz or 300 Hz in accordance with a Gaussian distribution at one of the two sites. The electric stimuli were applied for 120 seconds, three times at each location. To evaluate the impact of stimulus frequency and location on swallowing movements, we compared four groups (n=4 in each group). Following stimulation at one site, identical waveforms of stimulation were administered to both the back and neck for 120 seconds, three times. Swallowing was induced by water injection into the mouth after each stimulation. Electromyography (EMG) of three swallow-related muscles (mylohyoid, thyroarytenoid, and thyropharyngeus) was recorded and their amplitudes compared, both before stimulation (control) and after six consecutive stimulations. Results: It was observed that stimulation of both the back and neck resulted in larger maximum muscle potentials in all measured cases. Furthermore, the lower frequency of 30 Hz yielded larger muscle potentials than 300 Hz. Statistical analysis revealed that back stimulation led to early activation of the thyropharyngeus muscle compared to neck stimulation. Conclusion: The discovery that burst electrical stimulation with Gaussian frequency applied to the back activates swallow muscle groups suggests a potential novel swallow therapy option. This work was supported by NIH grants HL 111215, HL 103415 and OT20D001983, the Craig H. Neilsen Foundation Pilot Research Grant 546714, Kentucky Spinal Cord and Head Injury Research Trust, the Commonwealth of Kentucky Challenge for Excellence, and Grant-in-Aid for JSPS Fellows Number JP23KJ0966. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Role of the caudal Nucleus Tractus Solitarius and the Postinspiratory Complex in swallow production

The production of swallowing and its coordination with breathing is essential for homeostasis in most organisms. The postinspiratory complex (PiCo) is thought to regulate the transition between swallowing and breathing. Activation of PiCo triggers swallow production and stimulates laryngeal activation in a respiratory phase specific manner. However, little is known about PiCo’s role in swallow generation, which is thought to be controlled by the caudal portion of the Nucleus Tractus Solitarius (cNTS), presumably the swallow pattern generator (SPG). We hypothesize the cNTS is necessary for PiCo to trigger a swallow. Using both male and female Vglut2cre mice, a cre-dependent ChR2 AAV was bilaterally injected into PiCo and a cre-dependent inhibitory DREADD was bilaterally injected into the cNTS, specifically targeting the interstitial (SolI) and intermediate (SolIM) solitary nucleus. Twenty-one days after injection, using our freely breathing urethane anesthetized mouse model, swallows were stimulated by 1) injection of water into the oral cavity and 2) optogenetic stimulation of PiCo neurons; before and after DREADD activator, clozapine-N-oxide (CNO). Swallow and laryngeal activity were measured via monopolar suction electrodes of the hypoglossal (XII) and vagus (X) nerves as well as bipolar electromyogram (EMG) of the submental, laryngeal complex and costal diaphragm muscles. Post-hoc histological analysis was done to confirm areas of interest were targeted. We found that after activation of the inhibitory DREADD, located in the cNTS, swallow was not evoked by water stimulation nor optogenetic stimulation. We propose that PiCo relies on direct feedback from the cNTS in order to trigger swallow production. NIH F32 HL160102. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Realization of Multiple Synapse Plasticity by Coexistence of Volatile and Nonvolatile Characteristics of Interface Type Memristor.

Studies on neuromorphic computing systems are becoming increasingly important in the big-data-processing era as these systems are capable of energy-efficient parallel data processing and can overcome the present limitations owing to the von Neumann bottleneck. The Pt/WOx/ITO resistive random-access memory device can be used to implement versatile synapse functions because it possesses both volatile and nonvolatile characteristics. The gradual increase and decrease in the current of the Pt/WOx/ITO device with its uniform resistance state for endurance and retention enables additional synaptic applications that can be controlled using electric pulses. If the volatile and nonvolatile device properties are set through rehearsal and forgetting processes, the device can emulate various synaptic behaviors, such as potentiation and depression, paired-pulse facilitation, post-tetanic potentiation, image training, Hebbian learning rules, excitatory postsynaptic current, and Pavlov's test. Furthermore, reservoir computing can be implemented for applications such as pattern generation and recognition. This emphasizes the various applications of future neuromorphic devices, demonstrating the various favorable characteristics of pulse-enhanced Pt/WOx/ITO devices.

Immunomodulatory Activity of Cytokines in Hypertension: A Vascular Perspective.

Cytokines play a crucial role in the structure and function of blood vessels in hypertension. Hypertension damages blood vessels by mechanisms linked to shear forces, activation of the renin-angiotensin-aldosterone and sympathetic nervous systems, oxidative stress, and a proinflammatory milieu that lead to the generation of neoantigens and damage-associated molecular patterns, ultimately triggering the release of numerous cytokines. Damage-associated molecular patterns are recognized by PRRs (pattern recognition receptors) and activate inflammatory mechanisms in endothelial cells, smooth muscle cells, perivascular nerves, and perivascular adipose tissue. Activated vascular cells also release cytokines and express factors that attract macrophages, dendritic cells, and lymphocytes to the blood vessels. Activated and differentiated T cells into Th1, Th17, and Th22 in secondary lymphoid organs migrate to the vessels, releasing specific cytokines that further contribute to vascular dysfunction and remodeling. This chronic inflammation alters the profile of endothelial and smooth muscle cells, making them dysfunctional. Here, we provide an overview of how cytokines contribute to hypertension by impacting the vasculature. Furthermore, we explore clinical perspectives about the modulation of cytokines as a potential therapeutic intervention to specifically target hypertension-linked vascular dysfunction.

Automatic Generation Technology of Scrap Denim Clothes in Quilting Art Patterns by Integrating Computer Vision.

The combination of computer vision and advanced Fuzzy C-Means algorithms, such as Hybrid OK-Means Fuzzy C-Means (HOFCM), provides a revolutionary way to automatically generate technology of Scrap Denim Clothes in Quilting Art Patterns. The research focuses on the creation of an automatic generating technique specifically designed for making quilting designs from scrap denim clothing. This study investigates the effectiveness of HOFCM in improving the process of creating quilted art patterns from discarded denim materials. Extensive experimentation and analysis have shown that HOFCM dramatically decreases computational time while improving clustering solution quality, particularly when dealing with huge, high-dimensional datasets like scrap denim cloth analysis. The study emphasizes HOFCM's potential to speed up the quilting art pattern generation process, providing designers and artists with a more efficient and precise way to transform old denim clothing into visually appealing designs. The findings highlight HOFCM's transformative impact on automatic generation technology for quilting art patterns, paving the path for innovation and originality in textile art and design. HOFCM significantly lowers time by up to 93.94% compared to regular Fuzzy C-Means on four real and one synthetic datasets. The worst-case scenario resulted in a 2.51% reduction in solution quality.

Symmetrization of quasi-regular patterns with periodic tilting of regular polygons

AbstractComputer-generated aesthetic patterns are widely used as design materials in various fields. The most common methods use fractals or dynamical systems as basic tools to create various patterns. To enhance aesthetics and controllability, some researchers have introduced symmetric layouts along with these tools. One popular strategy employs dynamical systems compatible with symmetries that construct functions with the desired symmetries. However, these are typically confined to simple planar symmetries. The other generates symmetrical patterns under the constraints of tilings. Although it is slightly more flexible, it is restricted to small ranges of tilings and lacks textural variations. Thus, we proposed a new approach for generating aesthetic patterns by symmetrizing quasi-regular patterns using general k-uniform tilings. We adopted a unified strategy to construct invariant mappings for k-uniform tilings that can eliminate texture seams across the tiling edges. Furthermore, we constructed three types of symmetries associated with the patterns: dihedral, rotational, and reflection symmetries. The proposed method can be easily implemented using GPU shaders and is highly efficient and suitable for complicated tiling with regular polygons. Experiments demonstrated the advantages of our method over state-of-the-art methods in terms of flexibility in controlling the generation of patterns with various parameters as well as the diversity of textures and styles.

Swallowing-like activity elicited in neonatal rat medullary slice preparation

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.

Integrated optimization of production planning and electric trucks charging and discharging scheduling

The majority of supply chains in any manufacturing industry rely on heavy trucks for their flow of material from suppliers to end-users. Transportation by these heavy trucks causes greenhouse gas emissions detrimental to the environment. To cope with challenges caused by environmental issues and energy crises, replacing heavy trucks with electric trucks seems to be a viable solution. They can be considered as an energy resource that supports grid-to-vehicle and vehicle-to-grid, enabling efficient energy management for smart manufacturing systems and supply chains. Proper scheduling of charging and discharging activities of electric trucks also supports the penetration of renewable energy sources with volatile energy generation. This paper encourages sustainable practices and proposes a mixed integer programming model for the simultaneous optimization of production planning and transportation scheduling of electric trucks in a smart manufacturing system. The uncertainty sources considered in this system include power generation of renewable energy, transportation patterns, and customer demand for products. The results show that utilizing electric trucks in smart manufacturing with real-time pricing, save up to 11% of the total cost compared to traditional manufacturing systems without electric trucks and renewable energies. This integration also increases the efficient utilization of renewable energies such as wind and solar powers. The savings decrease to 4% and 8% under flat and time-of-use electricity pricing strategies. The results also highlight the complexity of setting utilization level of electric trucks and renewable energy sources into well-implemented sustainability standards.

Chronic intermittent hypoxia reveals role of the Postinspiratory Complex in the mediation of normal swallow production.

Obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder that results in multiple bouts of intermittent hypoxia. OSA has many neurological and systemic comorbidities, including dysphagia, or disordered swallow, and discoordination with breathing. However, the mechanism in which chronic intermittent hypoxia (CIH) causes dysphagia is unknown. Recently, we showed the postinspiratory complex (PiCo) acts as an interface between the swallow pattern generator (SPG) and the inspiratory rhythm generator, the preBötzinger complex, to regulate proper swallow-breathing coordination (Huff et al., 2023). PiCo is characterized by interneurons co-expressing transporters for glutamate (Vglut2) and acetylcholine (ChAT). Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly disrupts swallow behavior via triggering variable swallow motor patterns. This suggests that glutamatergic-cholinergic neurons in PiCo are not only critical for the regulation of swallow-breathing coordination, but also play an important role in the modulation of swallow motor patterning. Our study also suggests that swallow disruption, as seen in OSA, involves central nervous mechanisms interfering with swallow motor patterning and laryngeal activation. These findings are crucial for understanding the mechanisms underlying dysphagia, both in OSA and other breathing and neurological disorders.

Chronic intermittent hypoxia reveals role of the Postinspiratory Complex in the mediation of normal swallow production

Obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder that results in multiple bouts of intermittent hypoxia. OSA has many neurological and systemic comorbidities, including dysphagia, or disordered swallow, and discoordination with breathing. However, the mechanism in which chronic intermittent hypoxia (CIH) causes dysphagia is unknown. Recently, we showed the postinspiratory complex (PiCo) acts as an interface between the swallow pattern generator (SPG) and the inspiratory rhythm generator, the preBötzinger complex, to regulate proper swallow-breathing coordination (Huff et al., 2023). PiCo is characterized by interneurons co-expressing transporters for glutamate (Vglut2) and acetylcholine (ChAT). Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly disrupts swallow behavior via triggering variable swallow motor patterns. This suggests that glutamatergic–cholinergic neurons in PiCo are not only critical for the regulation of swallow-breathing coordination, but also play an important role in the modulation of swallow motor patterning. Our study also suggests that swallow disruption, as seen in OSA, involves central nervous mechanisms interfering with swallow motor patterning and laryngeal activation. These findings are crucial for understanding the mechanisms underlying dysphagia, both in OSA and other breathing and neurological disorders.

Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits.

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.

All-fiber few-mode interference for complex azimuthal pattern generation

We report on an all-fiber setup capable of generating complex intensity patterns using interference of few guided modes. Comprised by a few-mode fiber (FMF) spliced to a multimodal interference (MMI) fiber device, the setup allows for obtaining different output patterns upon adjusting the phases and intensities of the modes propagating in the FMF. We analyze the output patterns obtained when exciting two family modes in the MMI device using different phase and intensity conditions for the FMF modal base. Using this simple experimental arrangement we are able to produce complex intensity patterns with radial and azimuthal symmetry. Moreover, our results suggest that this approach provides a means to generate beams with orbital angular momentum (OAM).

Sensorimotor Oscillations in Human Infants during an Innate Rhythmic Movement.

The relationship between cerebral rhythms and early sensorimotor development is not clear. In recent decades, evidence revealed a rhythmic modulation involving sensorimotor processing. A widely corroborated functional role of oscillatory activity is to coordinate the information flow across sensorimotor networks. Their activity is coordinated by event-related synchronisation and desynchronisation in different sensorimotor rhythms, which indicate parallel processes may be occurring in the neuronal network during movement. To date, the dynamics of these brain oscillations and early sensorimotor development are unexplored. Our study investigates the relationship between the cerebral rhythms using EEG and a typical rhythmic movement of infants, the non-nutritive sucking (NNS) behaviour. NNS is an endogenous behaviour that originates from the suck central pattern generator in the brainstem. We find, in 17 infants, that sucking frequency correlates with beta synchronisation within the sensorimotor area in two phases: one strongly anticipating (~3 s) and the other encompassing the start of the motion. These findings suggest that a beta synchronisation of the sensorimotor cortex may influence the sensorimotor dynamics of NNS activity. Our results reveal the importance of rapid brain oscillations in infants and the role of beta synchronisation and their possible role in the communication between cortical and deep generators.

Embedding Bifurcations into Pneumatic Artificial Muscle.

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed forcontrol.