Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.

Connectivity and network burst properties of in-vitro neuronal networks induced by a clustered structure with alginate hydrogel patterning

Modularity is one of the important structural properties that affect information processing and other functionalities of neuronal networks. Researchers have developed in-vitro clustered network models for reproducing the modularity, but it is still challenging to control the segregation and integration of several sub-populations of them. We cultured clustered networks with alginate patterning and collected the electrophysiological signals to investigate the changes in functional properties during the development. We built inter-connected neuronal clusters using alginate micro-patterning with a circular shape on the surface of the micro-electrode array. The neuronal clusters were enabled to be connected at 3 or 10 days-in-vitro (DIV) by removing the barrier. The neuronal signals from different types of networks were collected from 16 to 34 DIV, and functional characteristics were examined. Connectivity and burst motif analysis were carried out to find out the relation between the structure and function of the networks. Neuronal networks with clustered structure showed different activity properties from the random networks along the development. The clustered networks had more short-range connections compared to the random networks. In the network burst motif analysis, the clustered networks showed more various patterns and a slower propagation of the activation patterns. In this study, we successfully cultured neuronal networks with clustered structure, and the structure affected the functional properties. The network model suggested in this study will be a good solution for observing the effect of structure on function during their development.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00289-5.

Loss of neuron network coherence induced by virus-infected astrocytes: a model study

Coherent activations of brain neuron networks underlie many physiological functions associated with various behavioral states. These synchronous fluctuations in the electrical activity of the brain are also referred to as brain rhythms. At the cellular level, rhythmicity can be induced by various mechanisms of intrinsic oscillations in neurons or the network circulation of excitation between synaptically coupled neurons. One specific mechanism concerns the activity of brain astrocytes that accompany neurons and can coherently modulate synaptic contacts of neighboring neurons, synchronizing their activity. Recent studies have shown that coronavirus infection (Covid-19), which enters the central nervous system and infects astrocytes, can cause various metabolic disorders. Specifically, Covid-19 can depress the synthesis of astrocytic glutamate and gamma-aminobutyric acid. It is also known that in the post-Covid state, patients may suffer from symptoms of anxiety and impaired cognitive functions. We propose a mathematical model of a spiking neuron network accompanied by astrocytes capable of generating quasi-synchronous rhythmic bursting discharges. The model predicts that if the release of glutamate is depressed, normal burst rhythmicity will suffer dramatically. Interestingly, in some cases, the failure of network coherence may be intermittent, with intervals of normal rhythmicity, or the synchronization can disappear.

A Neurospheroid-Based Microrobot for Targeted Neural Connections in a Hippocampal Slice

Functional restoration by the re-establishment of cellular or neural connections remains a major challenge in targeted cell therapy and regenerative medicine. Recent advances in magnetically powered microrobots have shown potential for use in controlled and targeted cell therapy. In this study, a magnetic neurospheroid (Mag-Neurobot) that can form both structural and functional connections with an organotypic hippocampal slice (OHS) is assessed using an ex vivo model as a bridge toward in vivo application. The Mag-Neurobot consists of hippocampal neurons and superparamagnetic nanoparticles (SPIONs); it is precisely and skillfully manipulated by an external magnetic field. Furthermore, the results of patch-clamp recordings of hippocampal neurons indicate that neither the neuronal excitabilities nor the synaptic functions of SPION-loaded cells are significantly affected. Analysis of neural activity propagation using high-density multi-electrode arrays shows that the delivered Mag-Neurobot is functionally connected with the OHS. The applications of this study include functional verification for targeted cell delivery through the characterization of novel synaptic connections and the functionalities of transported and transplanted cells. The success of the Mag-Neurobot opens up new avenues of research and application; it offers a test platform for functional neural connections and neural regenerative processes through cell transplantation.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Synapse integrity and function: Dependence on protein synthesis and identification of potential failure points

Synaptic integrity and function depend on myriad proteins - labile molecules with finite lifetimes that need to be continually replaced with freshly synthesized copies. Here we describe experiments designed to expose synaptic (and neuronal) properties and functions that are particularly sensitive to disruptions in protein supply, identify proteins lost early upon such disruptions, and uncover potential, yet currently underappreciated failure points. We report here that acute suppressions of protein synthesis are followed within hours by reductions in spontaneous network activity levels, impaired oxidative phosphorylation and mitochondrial function, and, importantly, destabilization and loss of both excitatory and inhibitory postsynaptic specializations. Conversely, gross impairments in presynaptic vesicle recycling occur over longer time scales (days), as does overt cell death. Proteomic analysis identified groups of potentially essential 'early-lost' proteins including regulators of synapse stability, proteins related to bioenergetics, fatty acid and lipid metabolism, and, unexpectedly, numerous proteins involved in Alzheimer's disease pathology and amyloid beta processing. Collectively, these findings point to neuronal excitability, energy supply and synaptic stability as early-occurring failure points under conditions of compromised supply of newly synthesized protein copies.

Comparative study between radiofrequency-induced and muscimol-induced inhibition of cultured networks of cortical neuron

Previous studies have shown that spontaneously active cultured networks of cortical neuron grown planar microelectrode arrays are sensitive to radiofrequency (RF) fields and exhibit an inhibitory response more pronounced as the exposure time and power increase. To better understand the mechanism behind the observed effects, we aimed at identifying similarities and differences between the inhibitory effect of RF fields (continuous wave, 1800 MHz) to the γ-aminobutyric acid type A (GABA_A) receptor agonist muscimol (MU). Inhibition of the network bursting activity in response to RF exposure became apparent at an SAR level of 28.6 W/kg and co-occurred with an elevation of the culture medium temperature of ~1°C. Exposure to RF fields preferentially inhibits bursting over spiking activity and exerts fewer constraints on neural network bursting synchrony, differentiating it from a pharmacological inhibition with MU. Network rebound excitation, a phenomenon relying on the intrinsic properties of cortical neurons, was observed following the removal of tonic hyperpolarization after washout of MU but not in response to cessation of RF exposure. This implies that hyperpolarization is not the main driving force mediating the inhibitory effects of RF fields. At the level of single neurons, network inhibition induced by MU and RF fields occurred with reduced action potential (AP) half-width. As changes in AP waveform strongly influence efficacy of synaptic transmission, the narrowing effect on AP seen under RF exposure might contribute to reducing network bursting activity. By pointing only to a partial overlap between the inhibitory hallmarks of these two forms of inhibition, our data suggest that the inhibitory mechanisms of the action of RF fields differ from the ones mediated by the activation of GABA_A receptors.

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning

Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these "learning populations" had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.

High-Aspect-Ratio Nanoelectrodes Enable Long-Term Recordings of Neuronal Signals with Subthreshold Resolution

The further development of neurochips requires high-density and high-resolution recordings that also allow neuronal signals to be observed over a long period of time. Expanding fields of network neuroscience and neuromorphic engineering demand the multiparallel and direct estimations of synaptic weights, and the key objective is to construct a device that also records subthreshold events. Recently, 3D nanostructures with a high aspect ratio have become a particularly suitable interface between neurons and electronic devices, since the excellent mechanical coupling to the neuronal cell membrane allows very high signal-to-noise ratio recordings. In the light of an increasing demand for a stable, noninvasive and long-term recording at subthreshold resolution, a combination of vertical nanostraws with nanocavities is presented. These structures provide a spontaneous tight coupling with rat cortical neurons, resulting in high amplitude sensitivity and postsynaptic resolution capability, as directly confirmed by combined patch-clamp and microelectrode array measurements.

Coupling of in vitro Neocortical-Hippocampal Coculture Bursts Induces Different Spike Rhythms in Individual Networks

Brain-state alternation is important for long-term memory formation. Each brain state can be identified with a specific process in memory formation, e.g., encoding during wakefulness or consolidation during sleeping. The hippocampal-neocortical dialogue was proposed as a hypothetical framework for systems consolidation, which features different cross-frequency couplings between the hippocampus and distributed neocortical regions in different brain states. Despite evidence supporting this hypothesis, little has been reported about how information is processed with shifts in brain states. To address this gap, we developed an in vitro neocortical-hippocampal coculture model to study how activity coupling can affect connections between coupled networks. Neocortical and hippocampal neurons were cultured in two different compartments connected by a micro-tunnel structure. The network activity of the coculture model was recorded by microelectrode arrays underlying the substrate. Rhythmic bursting was observed in the spontaneous activity and electrical evoked responses. Rhythmic bursting activity in one compartment could couple to that in the other via axons passing through the micro-tunnels. Two types of coupling patterns were observed: slow-burst coupling (neocortex at 0.1–0.5 Hz and hippocampus at 1 Hz) and fast burst coupling (neocortex at 20–40 Hz and hippocampus at 4–10 Hz). The network activity showed greater synchronicity in the slow-burst coupling, as indicated by changes in the burstiness index. Network synchronicity analysis suggests the presence of different information processing states under different burst activity coupling patterns. Our results suggest that the hippocampal-neocortical coculture model possesses multiple modes of burst activity coupling between the cortical and hippocampal parts. With the addition of external stimulation, the neocortical-hippocampal network model we developed can elucidate the influence of state shifts on information processing.

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

Functional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ''MEA-ToolBox'', a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient's hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.

Functionalized Nanocellulose Drives Neural Stem Cells toward Neuronal Differentiation

Transplantation of differentiated and fully functional neurons may be a better therapeutic option for the cure of neurodegenerative disorders and brain injuries than direct grafting of neural stem cells (NSCs) that are potentially tumorigenic. However, the differentiation of NSCs into a large population of neurons has been a challenge. Nanomaterials have been widely used as substrates to manipulate cell behavior due to their nano-size, excellent physicochemical properties, ease of synthesis, and versatility in surface functionalization. Nanomaterial-based scaffolds and synthetic polymers have been fabricated with topology resembling the micro-environment of the extracellular matrix. Nanocellulose materials are gaining attention because of their availability, biocompatibility, biodegradability and bioactivity, and affordable cost. We evaluated the role of nanocellulose with different linkage and surface features in promoting neuronal differentiation. Nanocellulose coupled with lysine molecules (CNC-Lys) provided positive charges that helped the cells to attach. Embryonic rat NSCs were differentiated on the CNC-Lys surface for up to three weeks. By the end of the three weeks of in vitro culture, 87% of the cells had attached to the CNC-Lys surface and more than half of the NSCs had differentiated into functional neurons, expressing endogenous glutamate, generating electrical activity and action potentials recorded by the multi-electrode array.

Microdevice for directional axodendritic connectivity between micro 3D neuronal cultures

Neuronal cultures are widely used in neuroscience research. However, the randomness of circuits in conventional cultures prevents accurate in vitro modeling of cortical development and of the pathogenesis of neurological and psychiatric disorders. A basic feature of cortical circuits that is not captured in standard cultures of dissociated cortical cells is directional connectivity. In this work, a polydimethylsiloxane (PDMS)-based device that achieves directional connectivity between micro 3D cultures is demonstrated. The device consists of through-holes for micro three-dimensional (μ3D) clusters of cortical cells connected by microtrenches for axon and dendrite guidance. The design of the trenches relies in part on the concept of axonal edge guidance, as well as on the novel concept of specific dendrite targeting. This replicates dominant excitatory connectivity in the cortex, enables the guidance of the axon after it forms a synapse in passing (an "en passant" synapse), and ensures that directional selectivity is preserved over the lifetime of the culture. The directionality of connections was verified morphologically and functionally. Connections were dependent on glutamatergic synapses. The design of this device has the potential to serve as a building block for the reconstruction of more complex cortical circuits in vitro.

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.

Networks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation—synergistic integration—is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

Consolidation of memory traces in cultured cortical networks requires low cholinergic tone, synchronized activity and high network excitability

In systems consolidation, encoded memories are replayed by the hippocampus during slow-wave sleep (SWS), and permanently stored in the neocortex. Declarative memory consolidation is believed to benefit from the oscillatory rhythms and low cholinergic tone observed in this sleep stage, but underlying mechanisms remain unclear. To clarify the role of cholinergic modulation and synchronized activity in memory consolidation, we applied repeated electrical stimulation in mature cultures of dissociated rat cortical neurons with high or low cholinergic tone, mimicking the cue replay observed during systems consolidation under distinct cholinergic concentrations. In the absence of cholinergic input, these cultures display activity patterns hallmarked by network bursts, synchronized events reminiscent of the low frequency oscillations observed during SWS. They display stable activity and connectivity, which mutually interact and achieve an equilibrium. Electrical stimulation reforms the equilibrium to include the stimulus response, a phenomenon interpreted as memory trace formation. Without cholinergic input, activity was burst-dominated. First application of a stimulus induced significant connectivity changes, while subsequent repetition no longer affected connectivity. Presenting a second stimulus at a different electrode had the same effect, whereas returning to the initial stimuli did not induce further connectivity alterations, indicating that the second stimulus did not erase the 'memory trace' of the first. Distinctively, cultures with high cholinergic tone displayed reduced network excitability and dispersed firing, and electrical stimulation did not induce significant connectivity changes. We conclude that low cholinergic tone facilitates memory formation and consolidation, possibly through enhanced network excitability. Network bursts or SWS oscillations may merely reflect high network excitability.

Characterization of neuronal viability and network activity under microfluidic flow

BACKGROUND

Microfluidics technology has the potential to allow precise control of the temporal and spatial aspects of solute concentration, making it highly relevant for the study of volume transmission mechanisms in neural tissue. However, full utilization of this technology depends on understanding how microfluidic flow at the rates needed for rapid solution exchange affects neuronal viability and network activity.

NEW METHOD

We designed a tape-based pressurized microfluidic flow system that is simple to fabricate and can be attached to commercial microelectrode arrays. The device is multi-layered, allowing the inclusion of a porous polycarbonate membrane to isolate neuronal cultures from shear forces while maintaining diffusive exchange of solutes. We used this system to investigate how flow affected survival and spiking patterns of cultured hippocampal neurons.

RESULTS

Viability and network activity of the cultures were reduced in proportion to flow rate. However, shear reduction measures did not improve survival or spiking activity; media conditioning in conjunction with culture age proved to be the critical factors for network stability. Diffusion simulations indicate that dilution of a small molecule accounts for the deleterious effects of flow on neuronal cultures.

COMPARISON WITH EXISTING METHODS

This work establishes the experimental conditions for real time measurement of network activity during rapid solution exchange, using multi-layered chambers with reversible bonding that allow for reuse of microelectrode arrays.

CONCLUSIONS

With correct media conditioning, the microfluidic flow system allows drug delivery on a subsecond timescale without disruption of network activity or viability, enabling in vitro reproduction of volume transmission mechanisms.

Micro Three-Dimensional Neuronal Cultures Generate Developing Cortex-Like Activity Patterns

Studies aimed at neurological drug discovery have been carried out both in vitro and in vivo. In vitro cell culture models have showed potential as drug testing platforms characterized by high throughput, low cost, good reproducibility and ease of handling and observation. However, in vitro neuronal culture models are facing challenges in replicating in vivo-like activity patterns. This work reports an in vitro culture technique that is capable of producing micro three-dimensional (μ3D) cultures of only a few tens of neurons. The μ3D cultures generated by this method were uniform in size and density of neurons. These μ3D cultures had complex spontaneous synchronized neuronal activity patterns which were similar to those observed in the developing cortex and in much larger 3D cultures, but not in 2D cultures. Bursts could be reliably evoked by stimulation of single neurons. Synchronized bursts in μ3D cultures were abolished by inhibitors of glutamate receptors, while inhibitors of GABA_A receptors had a more complex effect. This pharmacological profile is similar to bursts in neonatal cortex. Since large numbers of reproducible μ3D cultures can be created and observed in parallel, this model of the developing cortex may find applications in high-throughput drug discovery experiments.

Deuterated Glutamate-Mediated Neuronal Activity on Micro-Electrode Arrays

The excitatory synaptic transmission is mediated by glutamate in neuronal networks of the mammalian brain. In addition to the synaptic glutamate, extra-synaptic glutamate is known to modulate the neuronal activity. In neuronal networks, glutamate uptake is an important role of neurons and glial cells for lowering the concentration of extracellular glutamate and to avoid the excitotoxicity by glutamate. Monitoring the spatial distribution of intracellular glutamate is important to study the uptake of glutamate, but the approach has been hampered by the absence of appropriate glutamate analogs that report the localization of glutamate. Deuterium-labeled glutamate (GLU-D) is a promising tracer for monitoring the intracellular concentration of glutamate, but physiological properties of GLU-D have not been studied. Here we study the effects of extracellular GLU-D for the neuronal activity by using primary cultured rat hippocampal neurons that form neuronal networks on microelectrodes array. The frequency of firing in the spontaneous activity of neurons increased with the increasing concentration of extracellular GLU-D. The frequency of synchronized burst activity in neurons increased similarly as we observed in the spontaneous activity. These changes of the neuronal activity with extracellular GLU-D were suppressed by antagonists of glutamate receptors. These results suggest that GLU-D can be used as an analog of glutamate with equivalent effects for facilitating the neuronal activity. We anticipate GLU-D developing as a promising analog of glutamate for studying the dynamics of glutamate during neuronal activity.

Presynaptic L-Type Ca2+ Channels Increase Glutamate Release Probability and Excitatory Strength in the Hippocampus during Chronic Neuroinflammation

Neuroinflammation is involved in the pathogenesis of several neurologic disorders, including epilepsy. Both changes in the input/output functions of synaptic circuits and cell Ca²⁺ dysregulation participate in neuroinflammation, but their impact on neuron function in epilepsy is still poorly understood. Lipopolysaccharide (LPS), a toxic byproduct of bacterial lysis, has been extensively used to stimulate inflammatory responses both in vivo and in vitro LPS stimulates Toll-like receptor 4, an important mediator of the brain innate immune response that contributes to neuroinflammation processes. Although we report that Toll-like receptor 4 is expressed in both excitatory and inhibitory mouse hippocampal neurons (both sexes), its chronic stimulation by LPS induces a selective increase in the excitatory synaptic strength, characterized by enhanced synchronous and asynchronous glutamate release mechanisms. This effect is accompanied by a change in short-term plasticity with decreased facilitation, decreased post-tetanic potentiation, and increased depression. Quantal analysis demonstrated that the effects of LPS on excitatory transmission are attributable to an increase in the probability of release associated with an overall increased expression of L-type voltage-gated Ca²⁺ channels that, at presynaptic terminals, abnormally contributes to evoked glutamate release. Overall, these changes contribute to the excitatory/inhibitory imbalance that scales up neuronal network activity under inflammatory conditions. These results provide new molecular clues for treating hyperexcitability of hippocampal circuits associated with neuroinflammation in epilepsy and other neurologic disorders.SIGNIFICANCE STATEMENT Neuroinflammation is thought to have a pathogenetic role in epilepsy, a disorder characterized by an imbalance between excitation/inhibition. Fine adjustment of network excitability and regulation of synaptic strength are both implicated in the homeostatic maintenance of physiological levels of neuronal activity. Here, we focused on the effects of chronic neuroinflammation induced by lipopolysaccharides on hippocampal glutamatergic and GABAergic synaptic transmission. Our results show that, on chronic stimulation with lipopolysaccharides, glutamatergic, but not GABAergic, neurons exhibit an enhanced synaptic strength and changes in short-term plasticity because of an increased glutamate release that results from an anomalous contribution of L-type Ca²⁺ channels to neurotransmitter release.

Auxiliary α2δ1 and α2δ3 Subunits of Calcium Channels Drive Excitatory and Inhibitory Neuronal Network Development

VGCCs are multisubunit complexes that play a crucial role in neuronal signaling. Auxiliary α2δ subunits of VGCCs modulate trafficking and biophysical properties of the pore-forming α1 subunit and trigger excitatory synaptogenesis. Alterations in the expression level of α2δ subunits were implicated in several syndromes and diseases, including chronic neuropathic pain, autism, and epilepsy. However, the contribution of distinct α2δ subunits to excitatory/inhibitory imbalance and aberrant network connectivity characteristic for these pathologic conditions remains unclear. Here, we show that α2δ1 overexpression enhances spontaneous neuronal network activity in developing and mature cultures of hippocampal neurons. In contrast, overexpression, but not downregulation, of α2δ3 enhances neuronal firing in immature cultures, whereas later in development it suppresses neuronal activity. We found that α2δ1 overexpression increases excitatory synaptic density and selectively enhances presynaptic glutamate release, which is impaired on α2δ1 knockdown. Overexpression of α2δ3 increases the excitatory synaptic density as well but also facilitates spontaneous GABA release and triggers an increase in the density of inhibitory synapses, which is accompanied by enhanced axonaloutgrowth in immature interneurons. Together, our findings demonstrate that α2δ1 and α2δ3 subunits play distinct but complementary roles in driving formation of structural and functional network connectivity during early development. An alteration in α2δ surface expression during critical developmental windows can therefore play a causal role and have a profound impact on the excitatory-to-inhibitory balance and network connectivity.SIGNIFICANCE STATEMENT The computational capacity of neuronal networks is determined by their connectivity. Chemical synapses are the main interface for transfer of information between individual neurons. The initial formation of network connectivity requires spontaneous electrical activity and the calcium channel-mediated signaling. We found that, in early development, auxiliary α2δ3 subunits of calcium channels foster presynaptic release of GABA, trigger formation of inhibitory synapses, and promote axonal outgrowth in inhibitory interneurons. In contrast, later in development, α2δ1 subunits promote the glutamatergic neurotransmission and synaptogenesis, as well as strongly enhance neuronal network activity. We propose that formation of connectivity in neuronal networks is associated with a concerted interplay of α2δ1 and α2δ3 subunits of calcium channels.

Foetal neural progenitors contribute to postnatal circuits formation ex vivo: an electrophysiological investigation

Neuronal progenitor cells (NPC) play an essential role in homeostasis of the central nervous system (CNS). Considering their ability to differentiate into specific lineages, their manipulation and control could have a major therapeutic impact for those CNS injuries or degenerative diseases characterized by neuronal cell loss. In this work, we established an in vitro co-culture and tested the ability of foetal NPC (fNPC) to integrate among post-mitotic hippocampal neurons and contribute to the electrical activity of the resulting networks. We performed extracellular electrophysiological recordings of the activity of neuronal networks and compared the properties of spontaneous spiking in hippocampal control cultures (HCC), fNPC, and mixed circuitries ex vivo. We further employed patch-clamp intracellular recordings to examine single-cell excitability. We report of the capability of fNPC to mature when combined to hippocampal neurons, shaping the profile of network activity, a result suggestive of newly formed connectivity ex vivo.

Loss of Snord116 alters cortical neuronal activity in mice: a preclinical investigation of Prader–Willi syndrome

Prader–Willi syndrome (PWS) is a neurodevelopmental disorder that is characterized by metabolic alteration and sleep abnormalities mostly related to rapid eye movement (REM) sleep disturbances. The disease is caused by genomic imprinting defects that are inherited through the paternal line. Among the genes located in the PWS region on chromosome 15 (15q11-q13), small nucleolar RNA 116 (Snord116) has been previously associated with intrusions of REM sleep into wakefulness in humans and mice. Here, we further explore sleep regulation of PWS by reporting a study with PWScrm+/p− mouse line, which carries a paternal deletion of Snord116. We focused our study on both macrostructural electrophysiological components of sleep, distributed among REMs and nonrapid eye movements. Of note, here, we study a novel electroencephalography (EEG) graphoelements of sleep for mouse studies, the well-known spindles. EEG biomarkers are often linked to the functional properties of cortical neurons and can be instrumental in translational studies. Thus, to better understand specific properties, we isolated and characterized the intrinsic activity of cortical neurons using in vitro microelectrode array. Our results confirm that the loss of Snord116 gene in mice influences specific properties of REM sleep, such as theta rhythms and, for the first time, the organization of REM episodes throughout sleep–wake cycles. Moreover, the analysis of sleep spindles present novel specific phenotype in PWS mice, indicating that a new catalog of sleep biomarkers can be informative in preclinical studies of PWS.

Familiarity Detection and Memory Consolidation in Cortical Assemblies

Humans have a large capacity of recognition memory (Dudai, 1997), a fundamental property of higher-order brain functions such as abstraction and generalization (Vogt and Magnussen, 2007). Familiarity is the first step towards recognition memory. We have previously demonstrated using unsupervised neural network simulations that familiarity detection of complex patterns emerges in generic cortical microcircuits with bidirectional synaptic plasticity. It is therefore meaningful to conduct similar experiments on biological neuronal networks to validate these results. Studies of learning and memory in dissociated rodent neuronal cultures remain inconclusive to date. Synchronized network bursts (SNBs) that occur spontaneously and periodically have been speculated to be an intervening factor. By optogenetically stimulating cultured cortical networks with random dot movies (RDMs), we were able to reduce the occurrence of SNBs, after which an ability for familiarity detection emerged: previously seen patterns elicited higher firing rates than novel ones. Differences in firing rate were distributed over the entire network, suggesting that familiarity detection is a system level property. We also studied the change in SNB patterns following familiarity encoding. Support vector machine (SVM) classification results indicate that SNBs may be facilitating memory consolidation of the learned pattern. In addition, using a novel network connectivity probing method, we were able to trace the change in synaptic efficacy induced by familiarity encoding, providing insights on the long-term impact of having SNBs in the cultures.

Modeling the effects of sinusoidal stimulation and synaptic plasticity on linked neural oscillators

The brain exhibits intrinsic oscillatory behavior, which plays a vital role in communication and information processing. Abnormalities in brain rhythms have been linked to numerous disorders, including depression and schizophrenia. Rhythmic electrical stimulation (e.g., transcranial magnetic stimulation and transcranial alternating current stimulation) has been used to modulate these oscillations and produce lasting changes in neural activity. In this computational study, we investigate the combined effects of sinusoidal stimulation and synaptic plasticity on model networks comprised of simple, tunable four-neuron oscillators. While not intended to model a specific brain circuit, this idealization was created to provide some intuition on how electrical modulation can induce plastic changes in the oscillatory state. Linked pairs of oscillators were stimulated with sinusoidal current, and their behavior was measured as a function of their intrinsic frequencies, inter-oscillator synaptic strengths, and stimulus strength and frequency. Under certain stimulus conditions, sinusoidal current can disrupt the network's natural firing patterns. Synaptic plasticity can induce weight imbalances that permanently change the characteristic firing behavior of the network. Grids of 100 oscillators with random frequencies were also subjected to a wide array of stimulus conditions. The characteristics of the post-stimulus network activity depend heavily on the stimulus frequency and amplitude as well as the initial strength of inter-oscillator connections. Synchronization arises at the network level from complex patterns of activity propagation, which are enhanced or disrupted by different stimuli. The findings may prove important to the design of novel neuromodulation treatments and techniques seeking to affect oscillatory activity in the brain.

Real-time fluorescence measurement of spontaneous activity in a high-density hippocampal network cultivated on a plasmonic dish

High-density cultured neuronal networks have been used to evaluate synchronized features of neuronal populations. Voltage-sensitive dye (VSD) imaging of a dissociated cultured neuronal network is a critical method for studying synchronized neuronal activity in single cells. However, the signals of VSD are generally too faint-that is, the signal-to-noise ratio (S/N) is too low-to detect neuronal activity. In our previous research, a silver (Ag) plasmonic chip enhanced the fluorescence intensity of VSD to detect spontaneous neural spikes on VSD imaging. However, no high-density network was cultivated on the Ag plasmonic chip, perhaps because of the chemical instability of the Ag surface. In this study, to overcome the instability of the chip, we used a chemically stable gold (Au) plasmonic dish, which was a plastic dish with a plasmonic chip pasted to the bottom, to observe neuronal activity in a high-density neuronal network. We expected that the S/N in real-time VSD imaging of the Au plasmonic chip would be improved compared to that of a conventional glass-bottomed dish, and we also expected to detect frequent neural spikes. The increase in the number of spikes when inhibitory neurotransmitter receptors were inhibited suggests that the spikes corresponded to neural activity. Therefore, real-time VSD imaging of an Au plasmonic dish was effective for measuring spontaneous network activity in a high-density neuronal network at the spatial resolution of a single cell.

Unique Features of Network Bursts Emerge From the Complex Interplay of Excitatory and Inhibitory Receptors in Rat Neocortical Networks

Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion channel kinetics, respectively. The fast inhibition of the activity is mediated through gamma-aminobutyric acid type A receptors (GABA_ARs). Although the AMPAR, NMDAR and GABA_AR kinetics have been biophysically characterized in detail at the monosynaptic level in a variety of brain areas, the unique features of NBs emerging from the kinetics and the complex interplay of these receptors are not well understood. The goal of this study is to analyze the contribution of fast GABA_ARs on AMPAR- and NMDAR- mediated spontaneous NB activity in dissociated neonatal rat cortical cultures at 3 weeks in vitro. The networks were probed by both acute and gradual application of each excitatory receptor antagonist and combinations of acute excitatory and inhibitory receptor antagonists. At the same time, the extracellular network-wide activity was recorded with microelectrode arrays (MEAs). We analyzed the characteristic NB measures extracted from NB rate profiles and the distributions of interspike intervals, interburst intervals, and electrode recruitment time as well as the similarity of spatio-temporal patterns of network activity under different receptor antagonists. We show that NBs were rapidly initiated and recruited as well as diversely propagated by AMPARs and temporally and spatially maintained by NMDARs. GABA_ARs reduced the spiking frequency in AMPAR-mediated networks and dampened the termination of NBs in NMDAR-mediated networks as well as slowed down the recruitment of activity in all networks. Finally, we show characteristic super bursts composed of slow NBs with highly repetitive spatio-temporal patterns in gradually AMPAR blocked networks. To the best of our knowledge, this study is the first to unravel in detail how the three main mediators of synaptic transmission uniquely shape the NB characteristics, such as the initiation, maintenance, recruitment and termination of NBs in cortical cell cultures in vitro.

Active High-Density Electrode Arrays: Technology and Applications in Neuronal Cell Cultures

Active high-density electrode arrays realized with complementary metal-oxide-semiconductor (CMOS) technology provide electrophysiological recordings from several thousands of closely spaced microelectrodes. This has drastically advanced the spatiotemporal recording resolution of conventional multielectrode arrays (MEAs). Thus, today's electrophysiology in neuronal cultures can exploit label-free electrical readouts from a large number of single neurons within the same network. This provides advanced capabilities to investigate the properties of self-assembling neuronal networks, to advance studies on neurotoxicity and neurodevelopmental alterations associated with human brain diseases, and to develop cell culture models for testing drug- or cell-based strategies for therapies.Here, after introducing the reader to this neurotechnology, we summarize the results of different recent studies demonstrating the potential of active high-density electrode arrays for experimental applications. We also discuss ongoing and possible future research directions that might allow for moving these platforms forward for screening applications.

Neural layer self-assembly in geometrically confined rat and human 3D cultures

Neurological disorders affect millions of Americans and this number is expected to rise with the aging population. Development of drugs to treat these disorders may be facilitated by improved in vitro models that faithfully reproduce salient features of the relevant brain regions. Current 3D culture methods face challenges with reliably reproducing microarchitectural features of brain morphology such as cortical or hippocampal layers. In this work, polydimethylsiloxane (PDMS) mini-wells were used to create low aspect ratio, adherent 3D constructs where neurons self-assemble into layers. Layer self-assembly was determined to depend on the size of the PDMS mini-well. Layer formation occurred in cultures composed of primary rat cortical neurons or human induced pluripotent stem cell-derived neurons and astrocytes and was robust and reproducible. Layered 3D constructs were found to have spontaneous neural activity characterized by long bursts similar to activity in the developing cortex. The responses of layered 3D cultures to drugs were more similar to in vivo data than those of 2D cultures. 3D constructs created with this method may be thus suitable as in vitro models for drug discovery for neurological disorders.

Klotho deficiency affects the spine morphology and network synchronization of neurons

Klotho-deficient mice rapidly develop cognitive impairment and show some evidence of the onset of neurodegeneration. However, it is impossible to investigate the long-term consequences on the brain because of the dramatic shortening of lifespan caused by systemic klotho deficiency. As klotho expression is downregulated with advancing organismal age, understanding the mechanisms of klotho action is important for developing novel strategies to support healthy brain aging. Previously, we reported that klotho-deficient mice show enhanced long-term potentiation prior to the onset of cognitive impairment. To inform this unusual phenotype, herein, we examined neuronal structure and in vitro synaptic function. Our results indicate that klotho deficiency causes the population of dendritic spines to shift towards increased head diameter and decreased length consistent with mature, mushroom type spines. Multi-electrode array recordings from klotho-deficient neurons show increased synchronous firing and activity changes reflective of increased neuronal network activity. Supplementation of the neuronal growth media with recombinant shed klotho corrected some but not all of the activity changes caused by klotho deficiency. Last, in vivo we found that klotho-deficient mice have a decreased latency to induced seizure activity. Together these data show that klotho-deficient memory impairments are underpinned by structural and functional changes that may preclude ongoing normal cognition.

Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuron-glia co-culture on a multi-electrode array

The brain’s extracellular matrix (ECM) is a macromolecular network composed of glycosaminoglycans, proteoglycans, glycoproteins, and fibrous proteins. In vitro studies often use purified ECM proteins for cell culture coatings, however these may not represent the molecular complexity and heterogeneity of the brain’s ECM. To address this, we compared neural network activity (over 30 days in vitro) from primary neurons co-cultured with glia grown on ECM coatings from decellularized brain tissue (bECM) or MaxGel, a non-tissue-specific ECM. Cells were grown on a multi-electrode array (MEA) to enable noninvasive long-term interrogation of neuronal networks. In general, the presence of ECM accelerated the formation of networks without affecting the inherent network properties. However, specific features of network activity were dependent on the type of ECM: bECM enhanced network activity over a greater region of the MEA whereas MaxGel increased network burst rate associated with robust synaptophysin expression. These differences in network activity were not attributable to cellular composition, glial proliferation, or astrocyte phenotypes, which remained constant across experimental conditions. Collectively, the addition of ECM to neuronal cultures represents a reliable method to accelerate the development of mature neuronal networks, providing a means to enhance throughput for routine evaluation of neurotoxins and novel therapeutics.

Bundling of axons through a capillary alginate gel enhances the detection of axonal action potentials using microelectrode arrays

Microelectrode arrays (MEAs) have become important tools in high throughput assessment of neuronal activity. However, geometric and electrical constraints largely limit their ability to detect action potentials to the neuronal soma. Enhancing the resolution of these systems to detect axonal action potentials has proved both challenging and complex. In this study, we have bundled sensory axons from dorsal root ganglia through a capillary alginate gel (Capgel™) interfaced with an MEA and observed an enhanced ability to detect spontaneous axonal activity compared with two-dimensional cultures. Moreover, this arrangement facilitated the long-term monitoring of spontaneous activity from the same bundle of axons at a single electrode. Finally, using waveform analysis for cultures treated with the nociceptor agonist capsaicin, we were able to dissect action potentials from multiple axons on an individual electrode, suggesting that this model can reproduce the functional complexity associated with sensory fascicles in vivo. This novel three-dimensional functional model of the peripheral nerve can be used to study the functional complexities of peripheral neuropathies and nerve regeneration as well as being utilized in the development of novel therapeutics.

Long-Term Activity Dynamics of Single Neurons and Networks

The firing rate of neuronal spiking in vitro and in vivo significantly varies over extended timescales, characterized by long-memory processes and complex statistics, and appears in spontaneous as well as evoked activity upon repeated stimulus presentation. These variations in response features and their statistics, in face of repeated instances of a given physical input, are ubiquitous in all levels of brain-behavior organization. They are expressed in single neuron and network response variability but even appear in variations of subjective percepts or psychophysical choices and have been described as stemming from history-dependent, stochastic, or rate-determined processes.But what are the sources underlying these temporally rich variations in firing rate? Are they determined by interactions of the nervous system as a whole, or do isolated, single neurons or neuronal networks already express these fluctuations independent of higher levels? These questions motivated the application of a method that allows for controlled and specific long-term activation of a single neuron or neuronal network, isolated from higher levels of cortical organization.This chapter highlights the research done in cultured cortical networks to study (1) the inherent non-stationarity of neuronal network activity, (2) single neuron response fluctuations and underlying processes, and (3) the interface layer between network and single cell, the non-stationary efficacy of the ensemble of synapses impinging onto the observed neuron.

Burst Detection Methods

'Bursting', defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.

In Vitro Models of Brain Disorders

The brain is the most complex organ of the body, and many pathological processes underlying various brain disorders are poorly understood. Limited accessibility hinders observation of such processes in the in vivo brain, and experimental freedom is often insufficient to enable informative manipulations. In vitro preparations (brain slices or cultures of dissociated neurons) offer much better accessibility and reduced complexity and have yielded valuable new insights into various brain disorders. Both types of preparations have their advantages and limitations with regard to lifespan, preservation of in vivo brain structure, composition of cell types, and the link to behavioral outcome is often unclear in in vitro models. While these limitations hamper general usage of in vitro preparations to study, e.g., brain development, in vitro preparations are very useful to study neuronal and synaptic functioning under pathologic conditions. This chapter addresses several brain disorders, focusing on neuronal and synaptic functioning, as well as network aspects. Recent progress in the fields of brain circulation disorders, excitability disorders, and memory disorders will be discussed, as well as limitations of current in vitro models.

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks

As with other modern sciences (and their computational counterparts), neuroscience experiments can now produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. It is relatively common to be faced with datasets containing many millions of neural spikes collected from tens of thousands of neurons. Traditional data analysis methods can, in a relatively straightforward manner, identify large-scale features in such data (e.g., on the scale of entire networks). What these approaches often cannot do is to connect macroscopic activity to the relevant small-scale behaviors of individual cells, especially in the face of ongoing background activity that is not relevant. This communication presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale behavior, reducing data complexity to a level that can be amenable to further analysis. A small number of spatiotemporal spikes (among many millions) were found to provide reliable information about if and where a burst will occur.

Exogenous α-synuclein hinders synaptic communication in cultured cortical primary rat neurons

Amyloid aggregates of the protein α-synuclein (αS) called Lewy Bodies (LB) and Lewy Neurites (LN) are the pathological hallmark of Parkinson's disease (PD) and other synucleinopathies. We have previously shown that high extracellular αS concentrations can be toxic to cells and that neurons take up αS. Here we aimed to get more insight into the toxicity mechanism associated with high extracellular αS concentrations (50-100 μM). High extracellular αS concentrations resulted in a reduction of the firing rate of the neuronal network by disrupting synaptic transmission, while the neuronal ability to fire action potentials was still intact. Furthermore, many cells developed αS deposits larger than 500 nm within five days, but otherwise appeared healthy. Synaptic dysfunction clearly occurred before the establishment of large intracellular deposits and neuronal death, suggesting that an excessive extracellular αS concentration caused synaptic failure and which later possibly contributed to neuronal death.

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

Robot-Embodied Neuronal Networks as an Interactive Model of Learning

Background and Objective:

The reductionist approach of neuronal cell culture has been useful for analyses of synaptic signaling. Murine cortical neurons in culture spontaneously form an ex vivo network capable of transmitting complex signals, and have been useful for analyses of several fundamental aspects of neuronal development hitherto difficult to clarify in situ. However, these networks lack the ability to receive and respond to sensory input from the environment as do neurons in vivo. Establishment of these networks in culture chambers containing multi-electrode arrays allows recording of synaptic activity as well as stimulation.

Method:

This article describes the embodiment of ex vivo neuronal networks neurons in a closed-loop cybernetic system, consisting of digitized video signals as sensory input and a robot arm as motor output.

Results:

In this system, the neuronal network essentially functions as a simple central nervous system. This embodied network displays the ability to track a target in a naturalistic environment. These findings underscore that ex vivo neuronal networks can respond to sensory input and direct motor output.

Conclusion:

These analyses may contribute to optimization of neuronal-computer interfaces for perceptive and locomotive prosthetic applications. Ex vivo networks display critical alterations in signal patterns following treatment with subcytotoxic concentrations of amyloid-beta. Future studies including comparison of tracking accuracy of embodied networks prepared from mice harboring key mutations with those from normal mice, accompanied with exposure to Abeta and/or other neurotoxins, may provide a useful model system for monitoring subtle impairment of neuronal function as well as normal and abnormal development.

Positive feedback and synchronized bursts in neuronal cultures

Synchronized bursts (SBs) with complex structures are common in neuronal cultures. Although the phenomenon of SBs has been discovered for a long time, its origin is still unclear. Here, we investigate the properties of these SBs in cultures grown on a multi-electrode array. We find that structures of these SBs are related to the different developmental stages of the cultures and these structures can be modified by changing the magnesium concentration in the culture medium; indicating that synaptic mechanism is involved in the generation of SBs. A model based on short term synaptic plasticity (STSP), recurrent connections and astrocytic recycling of neurotransmitters has been developed successfully to understand the observed structures of SBs in experiments. A phase diagram obtained from this model shows that networks exhibiting SBs are in a complex oscillatory state due to large enough positive feedback provided by synaptic facilitation and recurrent connections. In this model, while STSP controls the fast oscillations (∼ 100 ms) within a SB, the astrocytic recycling determines the slow time scale (∼10 s) of inter-burst intervals. Our study suggests that glia-neuron interactions can be important in the understanding of the complex dynamics of neuronal networks.

Graphene Multielectrode Arrays as a Versatile Tool for Extracellular Measurements

Graphene multielectrode arrays (GMEAs) presented in this work are used for cardio and neuronal extracellular recordings. The advantages of the graphene as a part of the multielectrode arrays are numerous: from a general flexibility and biocompatibility to the unique electronic properties of graphene. The devices used for extensive in vitro studies of a cardiac-like cell line and cortical neuronal networks show excellent ability to extracellularly detect action potentials with signal to noise ratios in the range of 45 ± 22 for HL-1 cells and 48 ± 26 for spontaneous bursting/spiking neuronal activity. Complex neuronal bursting activity patterns as well as a variety of characteristic shapes of HL-1 action potentials are recorded with the GMEAs. This paper illustrates that the potential applications of the GMEAs in biological and medical research are still numerous and diverse.

Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate how the bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results show that the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.

Mesoscale Architecture Shapes Initiation and Richness of Spontaneous Network Activity

Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks in vivo and in vitro The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connectivity may foster the generation, maintenance, and richness of spontaneous activity. Since this mesoscale architecture cannot be systematically modified in intact tissue, testing these predictions is impracticable in vivo Here, we investigate how the mesoscale structure shapes spontaneous activity in generic networks of rat cortical neurons in vitro In these networks, neurons spontaneously arrange into local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and fasciculation of axons, i.e., promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons into clusters, local connectivity, and bundling of long-range axons. Supporting predictions from theory, clustered networks were more spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.SIGNIFICANCE STATEMENT Computational studies predict richer and persisting spatiotemporal patterns of spontaneous activity in neuronal networks with neuron clustering. To test this, we created networks of varying architecture in vitro Supporting these predictions, the generation and spatiotemporal patterns of propagation were most variable in networks with intermediate clustering and lowest in uniform networks. Grid-like clustering, on the other hand, facilitated spontaneous activity but led to degenerating patterns of propagation. Neurons outside clusters had weaker synaptic input than neurons within clusters, in which increased membrane potential fluctuations facilitated the initiation of synchronized spike activity. Our results thus show that the intermediate level organization of neuronal networks strongly influences the dynamics of their activity.