Synchronous Bursts Research Articles

Suppression of chaotic bursting synchronization in clustered scale-free networks by an external feedback signal.

Oscillatory activities in the brain, detected by electroencephalograms, have identified synchronization patterns. These synchronized activities in neurons are related to cognitive processes. Additionally, experimental research studies on neuronal rhythms have shown synchronous oscillations in brain disorders. Mathematical modeling of networks has been used to mimic these neuronal synchronizations. Actually, networks with scale-free properties were identified in some regions of the cortex. In this work, to investigate these brain synchronizations, we focus on neuronal synchronization in a network with coupled scale-free networks. The networks are connected according to a topological organization in the structural cortical regions of the human brain. The neuronal dynamic is given by the Rulkov model, which is a two-dimensional iterated map. The Rulkov neuron can generate quiescence, tonic spiking, and bursting. Depending on the parameters, we identify synchronous behavior among the neurons in the clustered networks. In this work, we aim to suppress the neuronal burst synchronization by the application of an external perturbation as a function of the mean-field of membrane potential. We found that the method we used to suppress synchronization presents better results when compared to the time-delayed feedback method when applied to the same model of the neuronal network.

Features of brain electrical activity in adult patients with POLG-related disorders

Introduction. Epilepsy is a common feature of mitochondrial disorders, including those associated with mutations in the POLG gene. Nevertheless, brain electrical activity features of POLG-related disorders in adult patients have not been adequately studied. Objective. To study the features and characteristics of the electroencephalography (EEG) pattern in adult patients with POLG-related disorders. Material and methods. Eight patients were examined: 7 with SANDO (Sensory Ataxic Neuropathy, Dysarthria, Ophthalmoparesis) syndrome, and 1 with MEMSA (Myoclonic Epilepsy Myopathy Sensory Ataxia) syndrome; median age was 32.5 years. All patients underwent routine EEG monitoring using a 19-channel electroencephalograph according to the generally accepted method. Results. Epileptic seizures were found in 3 patients, for 2 of them – as the first manifestation of the disease. In 6 patients, theta waves predominated in the occipital regions. Of those 6 patients, in 5 bilateral synchronous bursts of theta and delta wave groups were identified being more prominent in the frontocentral regions; 4 patients had transient non-lateralized delta activity in the occipital and parieto-occipital brain regions. In all patients, opening eyes led to the depression of rhythms and burst suppression. After photostimulation, in 2 cases bilateral synchronous bursts of delta and theta wave groups were recorded predominantly in frontal lobes. In 3 patients during hyperventilation an increase in delta activity in the occipital lobes and bilateral synchronous bursts of delta wave groups were observed. Epileptiform activity was recorded in 2 cases. Conclusion. In adult patients with POLG-related disorders, regardless of the clinical manifestation, typical EEG features include generalized background slowing, theta and delta bursts in occipital lobes with their suppression by opening eyes.

A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus

• A new auditory neural circuit is presented. • A functional neuron model is obtained. • Two auditory neurons reach resonance synchronization without synapse coupling. A piezoelectric ceramic can convert external mechanical force and vibration into electric signals by producing distinct output voltage when certain deformation is induced. As a result, a piezoelectric ceramic in nonlinear circuit can be used as sensitive sensor for detecting external sound signals. In this paper, a piezoelectric ceramic is incorporated into a simple FithzHugh–Nagumo neural circuit for capturing and encoding external sound signals. The improved neural circuit and functional neuron models are obtained, and a variety of firing modes are reproduced in the neural activities by changing the external sound signals. For animals, two ears can receive and encode the external sound signals synchronously in effective way, and thus the synchronization between two auditory neurons. To explore the capacity of discernment and cooperation of two ears in the auditory system when external voice is applied, two piezoelectric sensing neurons (PSNs) are driven by the same external voice for detecting possible synchronization approach without any synapse coupling. It is found that two identical PSNs driven by the same periodical stimuli (external forces) can reach synchronous bursting, spiking, and periodical firings, respectively. In case of chaotic firing, the synchronization stability is dependent on the external forcing applied on the two PSNs. Furthermore, external additive noise is applied for considering stochastic forcing on the PSN, it is confirmed that two identical PSNs can reach kinds of synchronous firings, while some intermediate noise intensities seldom enhance the synchronization stability between two identical PSNs. The Hamilton energy in isolated PSN driven by external forcing is estimated, and this kind of non-coupling synchronization is explained as a kind of resonance synchronization. These results can be helpful to design functional auxiliary devices for those patients with hearing impairment.

Effects of burst-timing-dependent plasticity on synchronous behaviour in neuronal network

Brain plasticity or neuroplasticity refers to the ability of the nervous system to reorganise itself in response to stimuli. For instance, sensory and motor stimulation, memory formation, and learning depend on brain plasticity. Neuronal synchronisation can be enhanced or suppressed by the plasticity. Synchronisation is related to many functions in the brain, as well as to some brain disorders. One possible plasticity rule is the burst-timing-dependent plasticity (BTDP), that induces synaptic alteration according to the timing of neuronal bursts. In this work, we build a network of coupled Rulkov maps where the excitatory connections are randomly distributed. We consider the BTDP to study its effects on the synchronous neuronal activities. In our simulations, we observe that depending on the initial synaptic weights, the whole network or part of it can have its neuronal synchronisation improved. This increase can be reached by two different mechanisms, the initial burst synchronisation and random statistical coincidence. A mix of these two mechanism is also found in the network. BTDP can induce the formation of desynchronised and synchronised clusters that operate in different frequencies, but only if the noise level is low. Our results show possible mechanisms of cluster formation in burst neuronal networks. We also consider the BTDP rule on a small-world network and show that, depending on the initial connection strength, the network can exhibit local or non-local properties.

Synchronization of the neurons coupled with sequential developing electrical and chemical synapses.

There is some evidence representing the sequential formation and elimination of electrical and chemical synapses in particular brain regions. Relying on this feature, this paper presents a purely mathematical modeling study on the synchronization among neurons connected by transient electrical synapses transformed to chemical synapses over time. This deletion and development of synapses are considered consecutive. The results represent that the transient synapses lead to burst synchronization of the neurons while the neurons are resting when both synapses exist constantly. The period of the transitions and also the time of presence of electrical synapses to chemical ones are effective on the synchronization. The larger synchronization error is obtained by increasing the transition period and the time of chemical synapses' existence.

Neural Interactions in Developing Rhythmogenic Spinal Networks: Insights From Computational Modeling.

The mechanisms involved in generation of rhythmic locomotor activity in the mammalian spinal cord remain poorly understood. These mechanisms supposedly rely on both intrinsic properties of constituting neurons and interactions between them. A subset of Shox2 neurons was suggested to contribute to generation of spinal locomotor activity, but the possible cellular basis for rhythmic bursting in these neurons remains unknown. Ha and Dougherty (2018) recently revealed the presence of bidirectional electrical coupling between Shox2 neurons in neonatal spinal cords, which can be critically involved in neuronal synchronization and generation of populational bursting. Gap junctional connections found between functionally-related Shox2 interneurons decrease with age, possibly being replaced by increasing interactions through chemical synapses. Here, we developed a computational model of a heterogeneous population of neurons sparsely connected by electrical or/and chemical synapses and investigated the dependence of frequency of populational bursting on the type and strength of neuronal interconnections. The model proposes a mechanistic explanation that can account for the emergence of a synchronized rhythmic activity in the neuronal population and provides insights into the possible role of gap junctional coupling between Shox2 neurons in the spinal mechanisms for locomotor rhythm generation.

Network Physiology of Cortico-Muscular Interactions.

Skeletal muscle activity is continuously modulated across physiologic states to provide coordination, flexibility and responsiveness to body tasks and external inputs. Despite the central role the muscular system plays in facilitating vital body functions, the network of brain-muscle interactions required to control hundreds of muscles and synchronize their activation in relation to distinct physiologic states has not been investigated. Recent approaches have focused on general associations between individual brain rhythms and muscle activation during movement tasks. However, the specific forms of coupling, the functional network of cortico-muscular coordination, and how network structure and dynamics are modulated by autonomic regulation across physiologic states remains unknown. To identify and quantify the cortico-muscular interaction network and uncover basic features of neuro-autonomic control of muscle function, we investigate the coupling between synchronous bursts in cortical rhythms and peripheral muscle activation during sleep and wake. Utilizing the concept of time delay stability and a novel network physiology approach, we find that the brain-muscle network exhibits complex dynamic patterns of communication involving multiple brain rhythms across cortical locations and different electromyographic frequency bands. Moreover, our results show that during each physiologic state the cortico-muscular network is characterized by a specific profile of network links strength, where particular brain rhythms play role of main mediators of interaction and control. Further, we discover a hierarchical reorganization in network structure across physiologic states, with high connectivity and network link strength during wake, intermediate during REM and light sleep, and low during deep sleep, a sleep-stage stratification that demonstrates a unique association between physiologic states and cortico-muscular network structure. The reported empirical observations are consistent across individual subjects, indicating universal behavior in network structure and dynamics, and high sensitivity of cortico-muscular control to changes in autonomic regulation, even at low levels of physical activity and muscle tone during sleep. Our findings demonstrate previously unrecognized basic principles of brain-muscle network communication and control, and provide new perspectives on the regulatory mechanisms of brain dynamics and locomotor activation, with potential clinical implications for neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies.

Influence of Delayed Conductance on Neuronal Synchronization.

In the brain, the excitation-inhibition balance prevents abnormal synchronous behavior. However, known synaptic conductance intensity can be insufficient to account for the undesired synchronization. Due to this fact, we consider time delay in excitatory and inhibitory conductances and study its effect on the neuronal synchronization. In this work, we build a neuronal network composed of adaptive integrate-and-fire neurons coupled by means of delayed conductances. We observe that the time delay in the excitatory and inhibitory conductivities can alter both the state of the collective behavior (synchronous or desynchronous) and its type (spike or burst). For the weak coupling regime, we find that synchronization appears associated with neurons behaving with extremes highest and lowest mean firing frequency, in contrast to when desynchronization is present when neurons do not exhibit extreme values for the firing frequency. Synchronization can also be characterized by neurons presenting either the highest or the lowest levels in the mean synaptic current. For the strong coupling, synchronous burst activities can occur for delays in the inhibitory conductivity. For approximately equal-length delays in the excitatory and inhibitory conductances, desynchronous spikes activities are identified for both weak and strong coupling regimes. Therefore, our results show that not only the conductance intensity, but also short delays in the inhibitory conductance are relevant to avoid abnormal neuronal synchronization.

Synchronization and multistability in the coupled neurons with propagation and processing delays

The dynamics of spiking and bursting activities have attracted interest from scientists due to their significance in the brain and neural systems. In this work, the effects of processing delay, specifically the time delay in coupling, on the dynamical behaviors of the coupled Hindmarsh–Rose neurons with resting state are investigated. The results reveal that the processing delay can induce rich spiking patterns, such as anti-phase synchronous spiking, in-phase synchronous periodic bursting, anti-phase synchronous periodic bursting and anti-phase synchronous chaotic bursting, and maintained the bistability and tristability of different spiking patterns. This emphasizes that the processing delay-induced spiking patterns might not be observed in the diffusively coupled neurons with resting state with and without propagation delay. The results can help deepen the knowledge on neural synchronization and information processing in the neuronal network and brain.

Synchrony of Spontaneous Burst Firing between Retinal Ganglion Cells Across Species.

Neurons communicate with other neurons in response to environmental changes. Their goal is to transmit information to their targets reliably. A burst, which consists of multiple spikes within a short time interval, plays an essential role in enhancing the reliability of information transmission through synapses. In the visual system, retinal ganglion cells (RGCs), the output neurons of the retina, show bursting activity and transmit retinal information to the lateral geniculate neuron of the thalamus. In this study, to extend our interest to the population level, the burstings of multiple RGCs were simultaneously recorded using a multi-channel recording system. As the first step in network analysis, we focused on investigating the pairwise burst correlation between two RGCs. Furthermore, to assess if the population bursting is preserved across species, we compared the synchronized bursting of RGCs between marmoset monkey (callithrix jacchus), one species of the new world monkeys and mouse (C57BL/6J strain). First, monkey RGCs showed a larger number of spikes within a burst, while the inter-spike interval, burst duration, and inter-burst interval were smaller compared with mouse RGCs. Monkey RGCs showed a strong burst synchronization between RGCs, whereas mouse RGCs showed no correlated burst firing. Monkey RGC pairs showed significantly higher burst synchrony and mutual information than mouse RGC pairs did. Comprehensively, through this study, we emphasize that two species have a different bursting activity of RGCs and different burst synchronization suggesting two species have distinctive retinal processing.

Astrocytic Regulation of Synchronous Bursting in Cortical Cultures: From Local to Global.

Synchronous bursting (SB) is ubiquitous in neuronal networks and independent of network structure. Although it is known to be driven by glutamatergic neurotransmissions, its underlying mechanism remains unclear. Recent studies show that local glutamate recycle by astrocytes affects nearby neuronal activities, which indicate that the local dynamics might also be the origin of SBs in networks. We investigated the effects of local glutamate dynamics on SBs in both cultures developed on multielectrode array (MEA) systems and a tripartite synapse simulation. Local glutamate uptake by astrocytes was altered by pharmacological targeting of GLT-1 glutamate transporters, whereas neuronal firing activities and synaptic glutamate level was simultaneously monitored with MEA and astrocyte-specific glutamate sensors (intensity-based glutamate-sensing fluorescent reporter), respectively. Global SB properties were significantly altered on targeting GLT-1. Detailed simulation of a network with astrocytic glutamate uptake and recycle mechanisms, conforming with the experimental observations, shows that astrocytes function as a slow negative feedback to neuronal activities in the network. SB in the network can be realized as an alternation between positive and negative feedback in the neurons and astrocytes, respectively. An understanding of glutamate trafficking dynamics is of general application to explain how astrocyte malfunction can result in pathological seizure-like phenomena in neuronal systems.

EEG Features in Adolescent Females with Self-Injurious Behavior in Endogenous Depressions

Background: the study of clinical and neurophysiological aspects of non-suicidal self-injurious behavior (NSSI), as one of the forms and risk factors for suicidal behavior in adolescents, including those suffering from mental disorders, is an urgent medical and social scientific task. Objective: To identify the features of EEG in depressive adolescent females with NSSI compared with EEG of age norm. Patients and methods: the study included 60 female patients aged 16–25 years with NSSI in the structure of endogenous depressive conditions, and 20 healthy subjects of the same gender and age. Clinical, psychopathological, psychometric, neurophysiological and statistical methods were used. Topographic EEG mapping revealed differences in the background EEG quantitative parameters of two studied groups. Results and discussion: spectral power values of alpha-2 (9–11 Hz) and alpha-1 (8–9 Hz) EEG frequency components in occipital-parietal and temporal leads, of theta-2 activity (6–8 Hz) in central-parietal leads, as well as of delta activity (2–4 Hz) in frontal and anterior temporal leads were higher in the left hemisphere, reflected increased activation of the right hemisphere. Generalized bilaterally synchronous alpha-theta bursts were registered regularly in EEG of NSSI patients, as well. Conclusions: the spatial distribution of EEG frequency components in depressive patients with NSSI indicates relatively decreased functional state of the cortex, especially of the left hemisphere and of its anterior regions, responsible for voluntary control of activity, with higher level of activation of temporal regions of the right hemisphere, associated with formation of negative emotions, and increased excitability of brain limbic-diencephalic structures, that may underlie poor controlled impulsive behavior.

Design, fabrication and characterization of an electrical-explosively actuated MEMS flyer-accelerator inserted with parallel bridge foils

Parallel bridge foils (PBF) with four strip foils, which is derived from traditional single bridge foil (SBF), was designed to study the effect of convergence and collision of plasmas and shock waves on driving flyer. Firstly, Electro-thermal simulation of PBF was performed to analyze temperature distribution before melting, which predicted the synchronous burst characteristic of PBF. Subsequently, a capacitor discharging circuit was designed to initiate bridge foils, results indicated PBF reached higher burst power in shorter time compared with SBF due to better matching between PBF and the test circuit. The flow fields of electrical explosion of bridge foils were photographed by ultra-high-speed camera, which displayed PBF almost burst simultaneously. Moreover, PBF had wider and brighter flow field visualization than SBF owing to convergence and superposition of plasma beams. Most importantly, flyer-accelerators inserted with bridge foils were prepared by MEMS technology, and comparative analysis from PDV revealed MEMS flyer-accelerator inserted with PBF had access to better velocity performances, compared with that inserted with SBF. For instance, PBF flyer-accelerator spent mere 168 ns to 2325 m/s at 900 V/0.22 µF, but SBF flyer-accelerator took 335 ns to 1073 m/s. Finally, we proposed a mathematical model for explaining the enhancement effect of flyer velocity, which to some extent showed good agreement with experimentation.

Burst mechanisms and burst synchronization in a system of coupled type-I and type-II neurons

The rich dynamics of a system comprising of a Type-I neuron coupled to a Type-II neuron via an electrical synapse (gap junction) are explored in this paper. Diverse dynamical behaviour ranging from quiescence and periodic spiking, to bursting and burst synchronization, were observed for different coupling schemes. The bifurcation mechanisms underlying the various bursts observed were identified. We report a unique burst mechanism, based on a focus/node bifurcation, occurring for bidirectionally coupled neurons. We attempt to understand the physical basis for the transitions from one burst pattern to another and also between the different aforementioned forms of dynamical behaviour observed on varying the coupling strength, in both unidirectionally and bidirectionally coupled neurons. The various dynamical regimes of the coupled system are exhaustively studied and demarcated through parameter plots. Type-I and type-II neurons exhibit mutually phase synchronized burst patterns at large values of the coupling which tend towards complete synchronization on increasing the coupling strength. Such collective dynamical behaviour can have important implications in biological systems.

Intraoperative Cortical Asynchrony Predicts Abnormal Postoperative Electroencephalogram

Postoperative electroencephalograms (EEGs) can identify seizure activity and neurologic dysfunction in high-risk neonates requiring cardiac surgical procedures. Although intraoperative EEG monitoring is uncommon, variations in cerebral blood flow and temperature during antegrade cerebral perfusion (ACP) can manifest as cortical asynchrony during EEG monitoring. We hypothesized that intraoperative EEG cortical asynchrony would identify neonates at risk for abnormal postoperative EEG tracings. Neonates requiring ACP for cardiac repair or palliation had continuous baseline, intraoperative, and postoperative hemodynamic and EEG monitoring. Synchronous and asynchronous cortical bursts were quantified during (1) cooling before ACP, (2) ACP, and (3) rewarming. Asynchronous bursts were defined as interhemispheric variations in electrical voltage or frequency. Neonates were divided into 2 groups, those with and without an abnormal postoperative EEG, which was defined as either persistent asynchrony for more than 2 hours or seizure activity on EEG. Among 40 neonates, 296 asynchronous bursts were noted, most commonly during rewarming. Eight (20%) neonates had an abnormal postoperative EEG (seizure activity,n= 3; persistent asynchrony, n= 5). Baseline demographics and intraoperative hemodynamics were similar between the groups. However, the total number of intraoperative asynchronous bursts was greater in neonates with an abnormal postoperative EEG (17 [11, IQR:24] vs 3[IQR:1, 7]; P < .001). Multivariate analysis confirmed that the number of asynchronous bursts was independently associated with an abnormal postoperative EEG (odds ratio,1.35; confidence interval,:1.10, 1.65; P= .004). Neonates with a greater number of intraoperative asynchronous cortical bursts had an abnormal postoperative EEG.

A simple model of epileptic seizure propagation: Potassium diffusion versus axo-dendritic spread.

The mechanisms of epileptic discharge generation and spread are not yet fully known. A recently proposed simple biophysical model of interictal and ictal discharges, Epileptor-2, reproduces well the main features of neuronal excitation and ionic dynamics during discharge generation. In order to distinguish between two hypothesized mechanisms of discharge propagation, we extend the model to the case of two-dimensional propagation along the cortical neural tissue. The first mechanism is based on extracellular potassium diffusion, and the second is the propagation of spikes and postsynaptic signals along axons and dendrites. Our simulations show that potassium diffusion is too slow to reproduce an experimentally observed speed of ictal wavefront propagation (tenths of mm/s). By contrast, the synaptic mechanism predicts well the speed and synchronization of the pre-ictal bursts before the ictal front and the afterdischarges in the ictal core. Though this fact diminishes the role of diffusion and electrodiffusion, the model nevertheless highlights the role of potassium extrusion during neuronal excitation, which provides a positive feedback that changes at the ictal wavefront the balance of excitation versus inhibition in favor of excitation. This finding may help to find a target for a treatment to prevent seizure propagation.

Coordinated activities of retrosplenial ensembles during resting-state encode spatial landmarks.

The brain likely uses offline periods to consolidate recent memories. One hypothesis holds that the hippocampal output provides a unique, global linking or 'index' code for each memory, and that this code is stored in the cortex in association with locally encoded attributes of each memory. Activation of the index code is hypothesized to evoke coordinated memory trace reactivation thus facilitating consolidation. Retrosplenial cortex (RSC) is a major recipient of hippocampal outflow and we have described populations of neurons there with sparse and orthogonal coding characteristics that resemble hippocampal 'place' cells, and whose expression depends on an intact hippocampus. Using two-photon Ca2+ imaging, we recorded ensembles of neurons in the RSC during periods of immobility before and after active running on a familiar linear treadmill track. Synchronous bursting of distinct groups of neurons occurred during rest both prior to and after running. In the second rest epoch, these patterns were associated with the locations of tactile landmarks and reward. Complementing established views on the functions of the RSC, our findings indicate that the structure is involved with processing landmark information during rest. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

M.3243A > G-Induced Mitochondrial Dysfunction Impairs Human Neuronal Development and Reduces Neuronal Network Activity and Synchronicity

Epilepsy, intellectual and cortical sensory deficits, and psychiatric manifestations are the most frequent manifestations of mitochondrial diseases. How mitochondrial dysfunction affects neural structure and function remains elusive, mostly because of a lack of proper invitro neuronal model systems with mitochondrial dysfunction. Leveraging induced pluripotent stem cell technology, we differentiated excitatory cortical neurons (iNeurons) with normal (low heteroplasmy) and impaired (high heteroplasmy) mitochondrial function on an isogenic nuclear DNA background from patients with the common pathogenic m.3243A > G variant of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS). iNeurons with high heteroplasmy exhibited mitochondrial dysfunction, delayed neural maturation, reduced dendritic complexity, and fewer excitatory synapses. Micro-electrode array recordings of neuronal networks displayed reduced network activity and decreased synchronous network bursting. Impaired neuronal energy metabolism and compromised structural and functional integrity of neurons and neural networks could be the primary drivers of increased susceptibility to neuropsychiatric manifestations of mitochondrial disease.

Object detection and tracking algorithms using brain-inspired model and deep neural networks

As the most effective bio-intelligence system, Human Visual System (HVS) has significant advantages in image processing, which helps to solve the problems in infrared target detection and tracking, such as dim small target, complex background, target occlusion and appearance changes, etc. In this paper, several brain-inspired models (including lateral inhibition, receptive field, synchronous burst, visual attention, and cognitive memory) and Deep Neural Networks (DNNs) have been studied, and the corresponding algorithms are proposed, which include: an infrared target detection algorithm based on lateral inhibition and singular value decomposition, an infrared target detection algorithm based on receptive field and lateral inhibition, an infrared moving dim target detection algorithm based on ALI-PCNN, an infrared target detection algorithm based on GCF-SB visual attention model, a kernel correlation filtering target tracking algorithm based on multi-channel memory model, and a robust and efficient discriminative-correlation-filter-based tracking approach based on the Response Map Analysis Network. Our experimental results show that the proposed algorithms are beneficial to achieve accurate infrared target detection and robust tracking under complex conditions.

Development of 3D neuromuscular bioactuators.

Neuronal control of skeletal muscle bioactuators represents a critical milestone toward the realization of future biohybrid machines that may generate complex motor patterns and autonomously navigate through their environment. Animals achieve these feats using neural networks that generate robust firing patterns and coordinate muscle activity through neuromuscular units. Here, we designed a versatile 3D neuron-muscle co-culture platform to serve as a test-bed for neuromuscular bioactuators. We used our platform in conjunction with microelectrode array electrophysiology to study the roles of synergistic interactions in the co-development of neural networks and muscle tissues. Our platform design enables co-culture of a neuronal cluster with up to four target muscle actuators, as well as quantification of muscle contraction forces. Using engineered muscle tissue targets, we first demonstrated the formation of functional neuromuscular bioactuators. We then investigated possible roles of long-range interactions in neuronal outgrowth patterns and observed preferential outgrowth toward muscles compared to the acellular matrix or fibroblasts, indicating muscle-specific chemotactic cues acting on motor neurons. Next, we showed that co-cultured muscle strips exhibited significantly higher spontaneous contractility as well as improved sarcomere assembly compared to muscles cultured alone. Finally, we performed microelectrode array measurements on neuronal cultures, which revealed that muscle-conditioned medium enhances overall neural firing rates and the emergence of synchronous bursting patterns. Overall, our study illustrates the significance of neuron-muscle cross talk for the in vitro development of neuromuscular bioactuators.