Learning in networks of cortical neurons

Neuromodulatory Compensation of Cortical Neural Activity on Electrodeposited Pt/Ir Modified Microelectrode Arrays for Temperature Transients

Temperature has a profound influence on various neuromodulation processes and has emerged as a focal point. However, the effects of acute environmental temperature fluctuations on cultured cortical networks have been inadequately elucidated. To bridge this gap, we have developed a brain-on-a-chip platform integrating cortical networks and electrodeposited Pt/Ir modified microelectrode arrays (MEAs) with 3D-printed bear-shaped triple chambers, facilitating control of temperature transients. This innovative system administers thermal stimuli while concurrently monitoring neuronal activity, including spikes and local field potentials, from 60 microelectrodes (diameter: 30 μm; impedance: 9.34 ± 1.37 kΩ; and phase delay: -45.26 ± 2.85°). Temperature transitions of approximately ±10 °C/s were applied to cortical networks on MEAs via in situ perfusion within the triple chambers. Subsequently, we examined the spatiotemporal dynamics of the brain-on-a-chip under temperature regulation at both the group level (neuronal population) and their interactions (network dynamics) and the individual level (cellular activity). Specifically, we found that after the temperature reduction neurons enhanced the overall information transmission efficiency of the network through synchronous firing to compensate for the decreased efficiency of single-cell level information transmission, in contrast to temperature elevation. By leveraging the integration of high-performance MEAs with perfusion chambers, this investigation provides a comprehensive understanding of the impact of temperature on the spatiotemporal dynamics of neural networks, thereby facilitating future exploration of the intricate interplay between temperature and brain function.

Neuronal avalanche dynamics regulated by spike-timing-dependent plasticity under different topologies and heterogeneities

Neuronal avalanches, a critical state of network self-organization, have been widely observed in electrophysiological records at different signal levels and spatial scales of the brain, which has significant influence on information transmission and processing in the brain. In this paper, the collective behavior of neuron firing is studied based on Leaky Integrate-and-Fire model and we induce spike-timing-dependent plasticity (STDP) to update the connection weight through competition between adjacent neurons in different network topologies. The result shows that STDP can facilitate the synchronization of the network and increase the probability of large-scale neuron avalanche obviously. Moreover, both the structure of STDP and network connection density can affect the generation of avalanche critical states, specifically, learning rate has positive correlation effect on the slope of power-law distribution and time constant has negative correction on it. However, when we the increase of heterogeneity in network, STDP can only has obvious promotion in synchrony under suitable level of heterogeneity. And we find that the process of long-term potentiation is sensitive to the adjustment of time constant and learning rate, unlike long-term depression, which is only sensitive to learning rate in heterogeneity network. It is suggested that presented results could facilitate our understanding on synchronization in various neural networks under the effect of STDP learning rules.

Revealing and reshaping attractor dynamics in large networks of cortical neurons

Attractors play a key role in a wide range of processes including learning and memory. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. By following the spontaneous network bursts of cultured cortical networks, we are able to define a vocabulary of spatiotemporal patterns and show that they function as discrete attractors in the network dynamics. We show that electrically stimulating specific attractors eliminates them from the spontaneous vocabulary, while they are still robustly evoked by the electrical stimulation. This seemingly paradoxical finding can be explained by a Hebbian-like strengthening of specific pathways into the attractors, at the expense of weakening non-evoked pathways into the same attractors. We verify this hypothesis and provide a mechanistic explanation for the underlying changes supporting this effect.

Investigating the reliability of the evoked response in human iPSCs-derived neuronal networks coupled to micro-electrode arrays

In vitro models of neuronal networks have emerged as a potent instrument for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human-induced pluripotent stem cells (hiPSCs) with micro-electrode arrays offers a means to replicate and dissect both the structural and functional elements of the human brain within a controlled in vitro environment. Given that neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation stands as a mean to comprehensively interrogate neuronal assemblies, to better understand their inherent electrophysiological dynamics. However, the establishment of standardized stimulation protocols for cultures derived from hiPSCs is still lacking, thereby hindering the precise delineation of efficacious parameters to elicit responses. To fill this gap, the primary objective of this study resides in delineating effective parameters for the electrical stimulation of hiPSCs-derived neuronal networks, encompassing the determination of voltage amplitude and stimulation frequency able to evoke reliable and stable responses. This study represents a stepping-stone in the exploration of efficacious stimulation parameters, thus broadening the electrophysiological activity profiling of neural networks sourced from human-induced pluripotent stem cells.

Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review

In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.

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.

Hybrid Life: Integrating biological, artificial, and cognitive systems

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural, and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and toward "life as it could be," with theoretical, synthetic, and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas, and contributions from a wide range of subjects. Hybrid Life brings our attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. Hybrid Life aims to cover studies that can lead to an understanding, from first principles, of what systems are and how biological and artificial systems can interact and integrate to form new kinds of hybrid (living) systems, individuals, and societies. To do so, it focuses on three complementary perspectives: theories of systems and agents, hybrid augmentation, and hybrid interaction. Theories of systems and agents are used to define systems, how they differ (e.g., biological or artificial, autonomous, or nonautonomous), and how multiple systems relate in order to form new hybrid systems. Hybrid augmentation focuses on implementations of systems so tightly connected that they act as a single, integrated one. Hybrid interaction is centered around interactions within a heterogeneous group of distinct living and nonliving systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022. This article is categorized under: Neuroscience > Cognition Philosophy > Artificial Intelligence Computer Science and Robotics > Robotics.

PPy/SWCNTs-Modified Microelectrode Array for Learning and Memory Model Construction through Electrical Stimulation and Detection of In Vitro Hippocampal Neuronal Network

The learning and memory functions of the brain remain unclear, which are in urgent need for the detection of both a single cell signal with high spatiotemporal resolution and network activities with high throughput. Here, an in vitro microelectrode array (MEA) was fabricated and further modified with polypyrrole/carboxylated single-walled carbon nanotubes (PPy/SWCNTs) nanocomposites as the interface between biological and electronic systems. The deposition of the nanocomposites significantly improved the performance of microelectrodes including low impedance (60.3 ± 28.8 k Ω), small phase delay (-32.8 ± 4.4°), and good biocompatibility. Then the modified MEA was used to apply learning training and test on hippocampal neuronal network cultured for 21 days through electrical stimulation, and multichannel electrophysiological signals were recorded simultaneously. During the process of learning training, the stimulus/response ratio of the hippocampal learning population gradually increased and the response time gradually decreased. After training, the mean spikes in burst, number of bursts, and mean burst duration increased by 53%, 191%, and 52%, respectively, and the correlation of neurons in the network was significantly enhanced from 0.45 ± 0.002 to 0.78 ± 0.002. In addition, the neuronal network basically retained these characteristics for at least 5 h. These results indicated that we have successfully constructed a learning and memory model of hippocampal neurons on the in vitro MEA, contributing to understanding learning and memory based on synaptic plasticity. The proposed PPy/SWCNTs-modified in vitro MEA will provide a promising platform for the exploration of learning and memory mechanism and their applications in vitro.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

Patients with Disorders of Consciousness: Are They Nonconscious, Unconscious, or Subconscious? Expanding the Discussion

Unprecedented advancements in the diagnosis and treatment of patients with disorders of consciousness (DoC) have given rise to ethical questions about how to recognize and respect autonomy and a sense of agency of the personhood when those capacities are themselves disordered, as they typically are in patients with DoC. At the intersection of these questions rests the distinction between consciousness and unconsciousness. Indeed, evaluations of consciousness levels and capacity for recovery have a significant impact on decisions regarding whether to discontinue or prolong life-sustaining therapy for DoC patients. However, in the unconsciousness domain, there is the confusing array of terms that are regularly used interchangeably, making it quite challenging to comprehend what unconsciousness is and how it might be empirically grounded. In this opinion paper, we will provide a brief overview of the state of the field of unconsciousness and show how a rapidly evolving electroencephalogram (EEG) neuroimaging technique may offer empirical, theoretical, and practical tools to approach unconsciousness and to improve our ability to distinguish consciousness from unconsciousness and also nonconsciousness with greater precision, particularly in cases that are borderline (as is typical in patients with DoC). Furthermore, we will provide a clear description of three distant notions of (un)consciousness (unconsciousness, nonconsciousness, and subconsciousness) and discuss how they relate to the experiential selfhood which is essential for comprehending the moral significance of what makes life worth living.

Human-Derived Cortical Neurospheroids Coupled to Passive, High-Density and 3D MEAs: A Valid Platform for Functional Tests

With the advent of human-induced pluripotent stem cells (hiPSCs) and differentiation protocols, methods to create in-vitro human-derived neuronal networks have been proposed. Although monolayer cultures represent a valid model, adding three-dimensionality (3D) would make them more representative of an in-vivo environment. Thus, human-derived 3D structures are becoming increasingly used for in-vitro disease modeling. Achieving control over the final cell composition and investigating the exhibited electrophysiological activity is still a challenge. Thence, methodologies to create 3D structures with controlled cellular density and composition and platforms capable of measuring and characterizing the functional aspects of these samples are needed. Here, we propose a method to rapidly generate neurospheroids of human origin with control over cell composition that can be used for functional investigations. We show a characterization of the electrophysiological activity exhibited by the neurospheroids by using micro-electrode arrays (MEAs) with different types (i.e., passive, C-MOS, and 3D) and number of electrodes. Neurospheroids grown in free culture and transferred on MEAs exhibited functional activity that can be chemically and electrically modulated. Our results indicate that this model holds great potential for an in-depth study of signal transmission to drug screening and disease modeling and offers a platform for in-vitro functional testing.

An Overview of In Vitro Biological Neural Networks for Robot Intelligence

In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.

Neuronal cultures playing Pong: First steps toward advanced screening and biological computing

In this issue of Neuron, Kagan et al.¹ implement learning-in-a-dish as an important step toward organoid intelligence. These systems may complement the study of molecular and cellular mechanisms of cognition and allow innovations in pharmacological and toxicological studies of neurodevelopmental or neurodegenerative disorders as well as advances in biological computing.

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.

Cancer progression as a learning process

Drug resistance and metastasis-the major complications in cancer-both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

Lipopolysaccharide-induced neuroinflammation disrupts functional connectivity and community structure in primary cortical microtissues

Three-dimensional (3D) neural microtissues are a powerful in vitro paradigm for studying brain development and disease under controlled conditions, while maintaining many key attributes of the in vivo environment. Here, we used primary cortical microtissues to study the effects of neuroinflammation on neural microcircuits. We demonstrated the use of a genetically encoded calcium indicator combined with a novel live-imaging platform to record spontaneous calcium transients in microtissues from day 14-34 in vitro. We implemented graph theory analysis of calcium activity to characterize underlying functional connectivity and community structure of microcircuits, which are capable of capturing subtle changes in network dynamics during early disease states. We found that microtissues cultured for 34 days displayed functional remodeling of microcircuits and that community structure strengthened over time. Lipopolysaccharide, a neuroinflammatory agent, significantly increased functional connectivity and disrupted community structure 5-9 days after exposure. These microcircuit-level changes have broad implications for the role of neuroinflammation in functional dysregulation of neural networks.

Spontaneous and Perturbational Complexity in Cortical Cultures

Dissociated cortical neurons in vitro display spontaneously synchronized, low-frequency firing patterns, which can resemble the slow wave oscillations characterizing sleep in vivo. Experiments in humans, rodents, and cortical slices have shown that awakening or the administration of activating neuromodulators decrease slow waves, while increasing the spatio-temporal complexity of responses to perturbations. In this study, we attempted to replicate those findings using in vitro cortical cultures coupled with micro-electrode arrays and chemically treated with carbachol (CCh), to modulate sleep-like activity and suppress slow oscillations. We adapted metrics such as neural complexity (NC) and the perturbational complexity index (PCI), typically employed in animal and human brain studies, to quantify complexity in simplified, unstructured networks, both during resting state and in response to electrical stimulation. After CCh administration, we found a decrease in the amplitude of the initial response and a marked enhancement of the complexity during spontaneous activity. Crucially, unlike in cortical slices and intact brains, PCI in cortical cultures displayed only a moderate increase. This dissociation suggests that PCI, a measure of the complexity of causal interactions, requires more than activating neuromodulation and that additional factors, such as an appropriate circuit architecture, may be necessary. Exploring more structured in vitro networks, characterized by the presence of strong lateral connections, recurrent excitation, and feedback loops, may thus help to identify the features that are more relevant to support causal complexity.

Using multielectrode arrays to investigate neurodegenerative effects of the amyloid-beta peptide

BACKGROUND

Multielectrode arrays are widely used to analyze the effects of potentially toxic compounds, as well as to evaluate neuroprotective agents upon the activity of neural networks in short- and long-term cultures. Multielectrode arrays provide a way of non-destructive analysis of spontaneous and evoked neuronal activity, allowing to model neurodegenerative diseases in vitro. Here, we provide an overview on how these devices are currently used in research on the amyloid-β peptide and its role in Alzheimer's disease, the most common neurodegenerative disorder.

MAIN BODY

Most of the studies analysed here indicate fast responses of neuronal cultures towards aggregated forms of amyloid-β, leading to increases of spike frequency and impairments of long-term potentiation. This in turn suggests that this peptide might play a crucial role in causing the typical neuronal dysfunction observed in patients with Alzheimer's disease.

CONCLUSIONS

Although the number of studies using multielectrode arrays to examine the effect of the amyloid-β peptide onto neural cultures or whole compartments is currently limited, they still show how this technique can be used to not only investigate the interneuronal communication in neural networks, but also making it possible to examine the effects onto synaptic currents. This makes multielectrode arrays a powerful tool in future research on neurodegenerative diseases.

The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data

The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users’ own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.

Embedding optimization reveals long-lasting history dependence in neural spiking activity

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long-potentially redundant-past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox.

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.

Spatial Memory in a Spiking Neural Network with Robot Embodiment

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

An Overview of Studies Demonstrating that ex vivo Neuronal Networks Display Multiple Complex Behaviors: Emergent Properties of Nearest-Neighbor Interactions of Excitatory and Inhibitory Neurons

The responsiveness of the human nervous system ranges from the basic sensory interpretation and motor regulation to so-called higher-order functions such as emotion and consciousness. Aspects of higher-order functions are displayed by other mammals and birds. In efforts to understand how neuronal interaction can generate such a diverse functionality, murine embryonic cortical neurons were cultured on Petri dishes containing multi-electrode arrays that allowed recording and stimulation of neuronal activity. Despite the lack of major architectural features that govern nervous system development in situ, this overview of multiple studies demonstrated that these 2-dimensional ex vivo neuronal networks nevertheless recapitulate multiple key aspects of nervous system development and activity in situ, including density-dependent, the spontaneous establishment of a functional network that displayed complex signaling patterns, and responsiveness to environmental stimulation including generation of appropriate motor output and long-term potentiation. These findings underscore that the basic interplay of excitatory and inhibitory neuronal activity underlies all aspects of nervous system functionality. This reductionist system may be useful for further examination of neuronal function under developmental, homeostatic, and neurodegenerative conditions.

Multi-curvature micropatterns unveil distinct calcium and mitochondrial dynamics in neuronal networks

Tangential curvatures are a key geometric feature of tissue folds in the human cerebral cortex. In the brain, these smoother and firmer bends are called gyri and sulci and form distinctive curved tissue patterns imposing a mechanical stimulus on neuronal networks. This stimulus is hypothesized to be essential for proper brain cell function but lacks in most standard neuronal cell assays. A variety of soft lithographic micropatterning techniques can be used to integrate round geometries in cell assays. Most microfabricated patterns, however, focus only on a small set of defined curvatures. In contrast, curvatures in the brain span a wide physical range, leaving it unknown which precise role distinct curvatures may play on neuronal cell signaling. Here we report a hydrogel-based multi-curvature design consisting of over twenty bands of distinct parallel curvature ranges to precisely engineer neuronal networks' growth and signaling under patterns of arcs. Monitoring calcium and mitochondrial dynamics in primary rodent neurons grown over two weeks in the multi-curvature patterns, we found that static calcium signaling was locally attenuated under higher curvatures (k > 0.01 μm-1). In contrast, to randomize growth, transient calcium signaling showed higher synchronicity when neurons formed networks in confined multi-curvature patterns. Additionally, we found that mitochondria showed lower motility under high curvatures (k > 0.01 μm-1) than under lower curvatures (k < 0.01 μm-1). Our results demonstrate how sensitive neuronal cell function may be linked and controlled through specific curved geometric features. Furthermore, the hydrogel-based multi-curvature design possesses high compatibility with various surfaces, allowing a flexible integration of geometric features into next-generation neuro devices, cell assays, tissue engineering, and implants.

Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks

Many indicators are involved in monitoring water quality. For instance, the fecal indicator bacteria are extremely important to detect the water quality. For this purpose, to better predict the total coliforms at the outlet of a Multi-Soil-Layering (MSL) system designed to treat domestic wastewater in rural areas, a neural network model has been developed and compared with linear regression model. The data was collected from the raw and treated wastewater of a three MSL systems during a one-year period in rural village, in Al-Haouz Province, Morocco. Fifteen physicochemical and bacteriological variables have undergone feature selection to select the best ones for predicting the total coliforms concentration in the effluent of MSL system. Furthermore, 80% of the available dataset were used to train and optimize the neural model using repeated cross validation technique. The remaining part (20%) was used to test the developed model. The neural network indicated excellent results compared to the linear regression. The optimal model was a neural network with one hidden layer and 11 neurons, where the R² was about 97%. The importance analysis of each predictor was established, and it was found that pH and total suspended solids had the greatest influence on the total coliforms removal.

Extracellular recording of direct synaptic signals with a CMOS-nanoelectrode array

The synaptic connections between neurons are traditionally determined by correlating the action potentials (APs) of a pre-synaptic neuron and small-amplitude subthreshold potentials of a post-synaptic neuron using invasive intracellular techniques, such as patch clamping. Extracellular recording by a microelectrode array can non-invasively monitor network activities of a large number of neurons, but its reduced sensitivity usually prevents direct measurements of synaptic signals. Here, we demonstrate that a newly developed complementary metal-oxide-semiconductor (CMOS) nanoelectrode array (CNEA) is capable of extracellularly determining direct synaptic connections in dense, multi-layer cultures of dissociated rat neurons. We spatiotemporally correlate action potential signals of hundreds of active neurons, detect small (∼1 pA after averaging) extracellular synaptic signals at the region where pre-synaptic axons and post-synaptic dendrites/somas overlap, and use those signals to map synaptic connections. We use controlled stimulation to assess stimulation-dependent synaptic strengths and to titrate a synaptic blocker (CNQX: IC50 ∼ 1 μM). The new capabilities demonstrated here significantly enhance the utilities of CNEAs in connectome mapping and drug screening applications.

Modelling and treating GRIN2A developmental and epileptic encephalopathy in mice

NMDA receptors play crucial roles in excitatory synaptic transmission. Rare variants in GRIN2A encoding the GluN2A subunit are associated with a spectrum of disorders, ranging from mild speech and language delay to intractable neurodevelopmental disorders, including but not limited to developmental and epileptic encephalopathy. A de novo missense variant, p.Ser644Gly, was identified in a child with this disorder, and Grin2a knock-in mice were generated to model and extend understanding of this intractable childhood disease. Homozygous and heterozygous mutant mice exhibited altered hippocampal morphology at 2 weeks of age, and all homozygotes exhibited lethal tonic-clonic seizures by mid-third week. Heterozygous adults displayed susceptibility to induced generalized seizures, hyperactivity, repetitive and reduced anxiety behaviours, plus several unexpected features, including significant resistance to electrically-induced limbic seizures and to pentylenetetrazole induced tonic-clonic seizures. Multielectrode recordings of neuronal networks revealed hyperexcitability and altered bursting and synchronicity. In heterologous cells, mutant receptors had enhanced NMDA receptor agonist potency and slow deactivation following rapid removal of glutamate, as occurs at synapses. NMDA receptor-mediated synaptic currents in heterozygous hippocampal slices also showed a prolonged deactivation time course. Standard anti-epileptic drug monotherapy was ineffective in the patient. Introduction of NMDA receptor antagonists was correlated with a decrease in seizure burden. Chronic treatment of homozygous mouse pups with NMDA receptor antagonists significantly delayed the onset of lethal seizures but did not prevent them. These studies illustrate the power of using multiple experimental modalities to model and test therapies for severe neurodevelopmental disorders, while revealing significant biological complexities associated with GRIN2A developmental and epileptic encephalopathy.

Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

Neuropsychiatric Disorders as Erratic Attention Regulation - Lessons from Electrophysiology

Attention related electrophysiological waves, such as P300, often deviate from norm in various populations of neuropsychiatric patients. For example, the amplitude is often smaller and the latency is often longer in major depressive disorder, in bipolar disorder and in schizophrenia. On the other hand, in other neuropsychiatric populations, it is often possible to note the opposite phenomena of larger P300 amplitude and shorter latency in comparison with norm, but only for a specific subset of stimuli. This is often reported in various anxiety disorders, substance abuse and various chronic pain syndromes. These findings in the various clinical populations, on their commonalities and differences, are presented in this work. The prevalence of these two types of deviations in the electrophysiological markers of attention, shared by multiple neuropsychiatric populations, raise interesting questions regarding the role of attention deviation and regulation in neuropsychiatry. We present these questions and outline a possible hypothesis in this regard. Furthermore, such potential sensitivity of the attention-related markers to clinical dynamics suggests they could be candidates for monitoring and, potentially, early-sensing of clinical dynamics. Therefore, we discuss the potential usability of such markers.

Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca²⁺ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca²⁺ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca²⁺ influx.

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.

Functional Scaffolding for Brain Implants: Engineered Neuronal Network by Microfabrication and iPSC Technology

Neuroengineering methods can be effectively used in the design of new approaches to treat central nervous system and brain injury caused by neurotrauma, ischemia, or neurodegenerative disorders. During the last decade, significant results were achieved in the field of implant (scaffold) development using various biocompatible and biodegradable materials carrying neuronal cells for implantation into the injury site of the brain to repair its function. Neurons derived from animal or human induced pluripotent stem (iPS) cells are expected to be an ideal cell source, and induction methods for specific cell types have been actively studied to improve efficacy and specificity. A critical goal of neuro-regeneration is structural and functional restoration of the injury site. The target treatment area has heterogeneous and complex network topology with various types of cells that need to be restored with similar neuronal network structure to recover correct functionality. However, current scaffold-based technology for brain implants operates with homogeneous neuronal cell distribution, which limits recovery in the damaged area of the brain and prevents a return to fully functional biological tissue. In this study, we present a neuroengineering concept for designing a neural circuit with a pre-defined unidirectional network architecture that provides a balance of excitation/inhibition in the scaffold to form tissue similar to that in the injured area using various types of iPS cells. Such tissue will mimic the surrounding niche in the injured site and will morphologically and topologically integrate into the brain, recovering lost function.

What would a synthetic connectome look like?

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

Inhomogeneities in Network Structure and Excitability Govern Initiation and Propagation of Spontaneous Burst Activity

The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in developing neuronal networks in vivo. Synchronous bursts are mostly thought to be triggered at burst initiation sites due to build-up of noise or by highly active neurons, or to reflect reverberating activity that circulates within larger networks, although neither of these has been observed directly. Inferring such collective dynamics in neuronal populations from electrophysiological recordings crucially depends on the spatial resolution and sampling ratio relative to the size of the networks assessed. Using large-scale microelectrode arrays with 1024 electrodes at 0.3 mm pitch that covered the full extent of in vitro networks on about 1 cm², we investigated where bursts of spontaneous activity arise and how their propagation patterns relate to the regions of origin, the network's structure, and to the overall distribution of activity. A set of alternating burst initiation zones (BIZ) dominated the initiation of distinct bursting events and triggered specific propagation patterns. Moreover, BIZs were typically located in areas with moderate activity levels, i.e., at transitions between hot and cold spots. The activity-dependent alternation between these zones suggests that the local networks forming the dominating BIZ enter a transient depressed state after several cycles (similar to Eytan et al., 2003), allowing other BIZs to take over temporarily. We propose that inhomogeneities in the network structure define such BIZs and that the depletion of local synaptic resources limit repetitive burst initiation.

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.

In vitro neural networks minimise variational free energy

In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle. In brief, we stimulated an in vitro cortical cell culture with stimulus trains that had a known statistical structure. We then asked whether recorded neuronal responses were consistent with variational message passing based upon free energy minimisation (i.e., evidence maximisation). Effectively, this required us to solve two problems: first, we had to formulate the Bayes-optimal encoding of the causes or sources of sensory stimulation, and then show that these idealised responses could account for observed electrophysiological responses. We describe a simulation of an optimal neural network (i.e., the ideal Bayesian neural code) and then consider the mapping from idealised in silico responses to recorded in vitro responses. Our objective was to find evidence for functional specialisation and segregation in the in vitro neural network that reproduced in silico learning via free energy minimisation. Finally, we combined the in vitro and in silico results to characterise learning in terms of trajectories in a variational information plane of accuracy and complexity.

Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity

Spiking neural networks (SNNs) are believed to be highly computationally and energy efficient for specific neurochip hardware real-time solutions. However, there is a lack of learning algorithms for complex SNNs with recurrent connections, comparable in efficiency with back-propagation techniques and capable of unsupervised training. Here we suppose that each neuron in a biological neural network tends to maximize its activity in competition with other neurons, and put this principle at the basis of a new SNN learning algorithm. In such a way, a spiking network with the learned feed-forward, reciprocal and intralayer inhibitory connections, is introduced to the MNIST database digit recognition. It has been demonstrated that this SNN can be trained without a teacher, after a short supervised initialization of weights by the same algorithm. Also, it has been shown that neurons are grouped into families of hierarchical structures, corresponding to different digit classes and their associations. This property is expected to be useful to reduce the number of layers in deep neural networks and modeling the formation of various functional structures in a biological nervous system. Comparison of the learning properties of the suggested algorithm, with those of the Sparse Distributed Representation approach shows similarity in coding but also some advantages of the former. The basic principle of the proposed algorithm is believed to be practically applicable to the construction of much more complicated and diverse task solving SNNs. We refer to this new approach as "Family-Engaged Execution and Learning of Induced Neuron Groups," or FEELING.

Random Neuronal Networks show homeostatic regulation of global activity while showing persistent changes in specific connectivity paths to theta burst stimuli

Learning in neuronal networks based on Hebbian principle has been shown to lead to destabilizing effects. Mechanisms have been identified that maintain homeostasis in such networks. However, the way in which these two opposing forces operate to support learning while maintaining stability is an active area of research. In this study, using neuronal networks grown on multi electrode arrays, we show that theta burst stimuli lead to persistent changes in functional connectivity along specific paths while the network maintains a global homeostasis. Simultaneous observations of spontaneous activity and stimulus evoked responses over several hours with theta burst training stimuli shows that global activity of the network quantified from spontaneous activity, which is disturbed due to theta burst stimuli is restored by homeostatic mechanisms while stimulus evoked changes in specific connectivity paths retain a memory trace of the training.

Cortical Network Synchrony Under Applied Electrical Field in vitro

Synchronous network activity plays a crucial role in complex brain functions. Stimulating the nervous system with applied electric field (EF) is a common tool for probing network responses. We used a gold wire-embedded silk protein film-based interface culture to investigate the effects of applied EFs on random cortical networks of in vitro cultures. Two-week-old cultures were exposed to EF of 27 mV/mm for <1 h and monitored by time-lapse calcium imaging. Network activity was represented by calcium signal time series mapped to source neurons and analyzed by using a community detection algorithm. Cortical cultures exhibited large scale, synchronized oscillations under alternating EF of changing frequencies. Field polarity and frequency change were both found to be necessary for network synchrony, as monophasic pulses of similar frequency changes or EF of a constant frequency failed to induce correlated activities of neurons. Group-specific oscillatory patterns were entrained by network-level synchronous oscillations when the alternating EF frequency was increased from 0.2 Hz to 200 kHz. Binary responses of either activity increase or decrease contributed to the opposite phase patterns of different sub-populations. Conversely, when the EF frequency decreased over the same range span, more complex behavior emerged showing group-specific amplitude and phase patterns. These findings formed the basis of a hypothesized network control mechanism for temporal coordination of distributed neuronal activity, involving coordinated stimulation by alternating polarity, and time delay by change of frequency. These novel EF effects on random neural networks have important implications for brain functional studies and neuromodulation applications.

Optogenetic Stimulation of Human Neural Networks Using Fast Ferroelectric Spatial Light Modulator—Based Holographic Illumination

The generation and application of human stem-cell-derived functional neural circuits promises novel insights into neurodegenerative diseases. These networks are often studied using stem-cell derived random neural networks in vitro, with electrical stimulation and recording using multielectrode arrays. However, the impulse response function of networks is best obtained with spatiotemporally well-defined stimuli, which electrical stimulation does not provide. Optogenetics allows for the functional control of genetically altered cells with light stimuli at high spatiotemporal resolution. Current optogenetic investigations of neural networks are often conducted using full field illumination, potentially masking important functional information. This can be avoided using holographically shaped illumination. In this article, we present a digital holographic illumination setup with a spatial resolution of about 8 µm, which suffices for the stimulation of single neurons, and offers a temporal resolution of less than 0.6 ms. With this setup, we present preliminary single-cell stimulation recording of stem-cell derived induced human neurons in a random neural network. This will offer the opportunity for further studies on connectivity in such networks.

Recovery of early neural spikes from stimulation electrodes using a DC-coupled low gain high resolution data acquisition system

BACKGROUND

Neural responses to electrical stimulation provide valuable information to probe and study the network function. Especially, recording neural responses from the stimulated site provides improved neural interfacing method. However, it is difficult to measure short-delayed responses at the stimulated electrode due to the saturation of the amplifier after stimulation which is called "stimulus artifact". Despite the advances in handling stimulation artifacts, it is still very challenging to deal with the artifacts if one tries to stimulate and record from the same electrode.

NEW METHOD

In this paper, we developed a system consisting of 24 bit ADC and low gain DC-amplifier which allows us to record the entire responses including saturation-free stimulus artifact and neural responses with excellent resolution.

RESULTS

Our approach showed saturation-free recording after stimulation, which makes it possible to recover neural spike as early as in 2 ms at the stimulating electrode with digital elimination methods.

COMPARISON WITH EXISTING METHODS

With our system we could record neural signals after stimulation that was difficult with high gain and high pass filtered recording system due to amplifier saturation.

CONCLUSIONS

Our new system can enhance interface performance with its higher robustness and with simple system configuration.

Self-Aligned Functionalization Approach to Order Neuronal Networks at the Single-Cell Level

Despite significant progress, our knowledge of the functioning of the central nervous system still remains scarce to date. A better understanding of its behavior, in either normal or diseased conditions, goes through an increased knowledge of basic mechanisms involved in neuronal function, including at the single-cell level. This has motivated significant efforts for the development of miniaturized sensing devices to monitor neuronal activity with high spatial and signal resolution. One of the main challenges remaining to be addressed in this domain is, however, the ability to create in vitro spatially ordered neuronal networks at low density with a precise control of the cell location to ensure proper monitoring of the activity of a defined set of neurons. Here, we present a novel self-aligned chemical functionalization method, based on a repellant surface with patterned attractive areas, which permits the elaboration of low-density neuronal network down to individual cells with a high control of the soma location and axonal growth. This approach is compatible with complementary metal-oxide-semiconductor line technology at a wafer scale and allows performing the cell culture on packaged chip outside microelectronics facilities. Rat cortical neurons were cultured on such patterned surfaces for over one month and displayed a very high degree of organization in large networks. Indeed, more than 90% of the network nodes were settled by a soma and 100% of the connecting lines were occupied by a neurite, with a very good selectivity (low parasitic cell connections). After optimization, networks composed of 75% of unicellular nodes were obtained, together with a control at the micron scale of the location of the somas. Finally, we demonstrated that the dendritic neuronal growth was guided by the surface functionalization, even when micrometer scale topologies were encountered and we succeeded to control the extension growth along one-dimensional-aligned nanostructures with sub-micrometrical scale precision. This novel approach now opens the way for precise monitoring of neuronal network activity at the single-cell level.

Action potential propagation recorded from single axonal arbors using multielectrode arrays

We report the presence of co-occurring extracellular action potentials (eAPs) from cultured mouse hippocampal neurons among groups of planar electrodes on multielectrode arrays (MEAs). The invariant sequences of eAPs among coactive electrode groups, repeated co-occurrences, and short interelectrode latencies are consistent with action potential propagation in unmyelinated axons. Repeated eAP codetection by multiple electrodes was widespread in all our data records. Codetection of eAPs confirms they result from the same neuron and allows these eAPs to be isolated from all other spikes independently of spike sorting algorithms. We averaged co-occurring events and revealed additional electrodes with eAPs that would otherwise be below detection threshold. We used these eAP cohorts to explore the temperature sensitivity of action potential propagation and the relationship between voltage-gated sodium channel density and propagation velocity. The sequence of eAPs among coactive electrodes "fingerprints" neurons giving rise to these events and identifies them within neuronal ensembles. We used this property and the noninvasive nature of extracellular recording to monitor changes in excitability at multiple points in single axonal arbors simultaneously over several hours, demonstrating independence of axonal segments. Over several weeks, we recorded changes in interelectrode propagation latencies and ongoing changes in excitability in different regions of single axonal arbors. Our work illustrates how repeated eAP co-occurrences can be used to extract physiological data from single axons with low-density MEAs. However, repeated eAP co-occurrences lead to oversampling spikes from single neurons and thus can confound traditional spike-train analysis. NEW & NOTEWORTHY We studied action potential propagation in single axons using low-density multielectrode arrays. We unambiguously identified the neuronal sources of propagating action potentials and recorded extracellular action potentials from several positions within single axonal arbors. We found a surprisingly high density of axonal voltage-gated sodium channels responsible for a high propagation safety factor. Our experiments also demonstrate that excitability in different segments of single axons is regulated independently on timescales from hours to weeks.

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.

Phytoremediation of domestic wastewater using a hybrid constructed wetland in mountainous rural area

The purpose of this study is to evaluate the efficiency of hybrid constructed wetlands (HCWs) in a rural mountainous area. The experiment was set up in small rural community named Tidili within the region of Marrakech, Morocco. The wastewater treatment plant was composed of three vertical flow constructed wetlands (VFCWs) working in parallel, followed by two parallel horizontal-subsurface flow constructed wetlands (HFCWs), with hydraulic loading rates of 0.5 and 0.75 m³/m².d, respectively. The two units were planted with Phragmites australis at a density of 4 plants/m². Wastewater samples were collected at the inlet of the storage tank and at the outlet of the whole system (VFCWs, HFCWs) stages. The main removal percentages of total suspended solids (TSS), biochemical oxygen demand measured in a 5-day test (BOD₅), chemical oxygen demand (COD), total nitrogen, and total phosphorus were respectively 95%, 93%, 91%, 67%, and 62%. The system showed a very high capacity to remove total coliforms, fecal coliforms, and fecal streptococci (4.46, 4.31, and 4.10 Log units, respectively). Artificial neural networks (ANNs) were used to model the quality parameters (TSS, BOD₅, COD) and total coliforms and fecal streptococci. Based on the obtained results, the ANN model could be considered as an efficient tool to predict the studied phytoremediation performances using HCWs.