Strengthening of synchronized activity by tetanic stimulation in cortical cultures: application of planar electrode arrays

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.

Engineering circuits of human iPSC-derived neurons and rat primary glia

Novel in vitro platforms based on human neurons are needed to improve early drug testing and address the stalling drug discovery in neurological disorders. Topologically controlled circuits of human induced pluripotent stem cell (iPSC)-derived neurons have the potential to become such a testing system. In this work, we build in vitro co-cultured circuits of human iPSC-derived neurons and rat primary glial cells using microfabricated polydimethylsiloxane (PDMS) structures on microelectrode arrays (MEAs). Our PDMS microstructures are designed in the shape of a stomach, which guides axons in one direction and thereby facilitates the unidirectional flow of information. Such circuits are created by seeding either dissociated cells or pre-aggregated spheroids at different neuron-to-glia ratios. Furthermore, an antifouling coating is developed to prevent axonal overgrowth in undesired locations of the microstructure. We assess the electrophysiological properties of different types of circuits over more than 50 days, including their stimulation-induced neural activity. Finally, we demonstrate the inhibitory effect of magnesium chloride on the electrical activity of our iPSC circuits as a proof-of-concept for screening of neuroactive compounds.

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.

Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders

Simple Summary

Multielectrode array technology allows researchers to record the spontaneous firing activity of cultured neurons over a period of multiple weeks or months. These data can be valuable for understanding how the functional relationships between neurons evolve as they begin to form connections and wire into a functional network. This technology has been adopted by researchers using stem cells to produce human neurons in culture to study neurodevelopmental disorders. However, the dizzying complexity and scale of the data generated have posed some challenges with the analysis and interpretation of experimental results. Here, we first provide historical context as to why multielectrode array platforms were originally developed, and use this perspective to explore some of the challenges currently facing the field. We then highlight new analysis methods, provide some guidance for improving the analysis of multielectrode array data, and discuss standardizing how these findings are communicated in scientific publications.

Abstract

In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.

A wake-like state in vitro induced by transmembrane TNF/soluble TNF receptor reverse signaling

Tumor necrosis factor alpha (TNF) has sleep regulatory and brain development roles. TNF promotes sleep in vivo and in vitro while TNF inhibition diminishes sleep. Transmembrane (tm) TNF and the tmTNF receptors (Rs), are cleaved by tumor necrosis factor alpha convertase to produce soluble (s) TNF and sTNFRs. Reverse signaling occurs in cells expressing tmTNF upon sTNFR binding. sTNFR administration in vivo inhibits sleep, thus we hypothesized that a wake-like state in vitro would be induced by sTNFR-tmTNF reverse signaling. Somatosensory cortical neuron/glia co-cultures derived from male and female mice lacking both TNFRs (TNFRKO), or lacking TNF (TNFKO) and wildtype (WT) mice were plated onto six-well multi-electrode arrays. Daily one-hour electrophysiological recordings were taken on culture days 4 through 14. sTNFR1 (0.0, 0.3, 3, 30, 60, and 120 ng/µL) was administered on day 14. A final one-hour recording was taken on day 15. Four measures were characterized that are also used to define sleep in vivo: action potentials (APs), burstiness index (BI), synchronization of electrical activity (SYN), and slow wave power (SWP; 0.25-3.75 Hz). Development rates of these emergent electrophysiological properties increased in cells from mice lacking TNF or both TNFRs compared to cells from WT mice. Decreased SWP, after the three lowest doses (0.3, 3 and 30 ng/µL) of the sTNFR1, indicate a wake-like state in cells from TNFRKO mice. A wake-like state was also induced after 3 ng/µl sTNFR1 treatment in cells from TNFKO mice, which express the TNFR1 ligand, lymphotoxin alpha. Cells from WT mice showed no treatment effects. Results are consistent with prior studies demonstrating involvement of TNF in brain development, TNF reverse signaling, and sleep regulation in vivo. Further, the current demonstration of sTNFR1 induction of a wake-like state in vitro is consistent with the idea that small neuronal/glial circuits manifest sleep- and wake-like states analogous to those occurring in vivo. Finally, that sTNF forward signaling enhances sleep while sTNFR1 reverse signaling enhances a wake-like state is consistent with other sTNF/tmTNF/sTNFR1 brain actions having opposing activities.

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.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Modulating electrophysiology of motor neural networks via optogenetic stimulation during neurogenesis and synaptogenesis

Control of electrical activity in neural circuits through network training is a grand challenge for biomedicine and engineering applications. Past efforts have not considered evoking long-term changes in firing patterns of in-vitro networks by introducing training regimens with respect to stages of neural development. Here, we used Channelrhodopsin-2 (ChR2) transfected mouse embryonic stem cell (mESC) derived motor neurons to explore short and long-term programming of neural networks by using optical stimulation implemented during neurogenesis and synaptogenesis. Not only did we see a subsequent increase of neurite extensions and synaptophysin clustering, but by using electrophysiological recording with micro electrode arrays (MEA) we also observed changes in signal frequency spectra, increase of network synchrony, coordinated firing of actions potentials, and enhanced evoked response to stimulation during network formation. Our results demonstrate that optogenetic stimulation during neural differentiation can result in permanent changes that extended to the genetic expression of neurons as demonstrated by RNA Sequencing. To our knowledge, this is the first time that a correlation between training regimens during neurogenesis and synaptogenesis and the resulting plastic responses has been shown in-vitro and traced back to changes in gene expression. This work demonstrates new approaches for training of neural circuits whose electrical activity can be modulated and enhanced, which could lead to improvements in neurodegenerative disease research and engineering of in-vitro multi-cellular living systems.

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.

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.

The neuron-specific interleukin-1 receptor accessory protein alters emergent network state properties in Vitro

Small in vitro neuronal/glial networks exhibit sleep-like states. Sleep regulatory substance interleukin-1β (IL1) signals via its type I receptor and a receptor accessory protein (AcP). AcP has a neuron-specific isoform called AcPb. After sleep deprivation, AcPb, but not AcP, upregulates in brain, and mice lacking AcPb lack sleep rebound. Herein we used action potentials (APs), AP burstiness, synchronization of electrical activity (SYN), and delta wave (0.5–3.75 Hz) power to characterize cortical culture network state. Homologous parameters are used in vivo to characterize sleep. Cortical cells from 1–2-day-old pups from AcP knockout (KO, lacking both AcP and AcPb), AcPb KO (lacking only AcPb), and wild type (WT) mice were cultured separately on multi-electrode arrays. Recordings of spontaneous activity were taken each day during days 4–14 in vitro. In addition, cultures were treated with IL1, or in separate experiments, stimulated electrically to determine evoked response potentials (ERPs). In AcP KO cells, the maturation of network properties accelerated compared to those from cells lacking only AcPb. In contrast, the lack of AcPb delayed spontaneous network emergence of sleep-linked properties. The addition of IL1 enhanced delta wave power in WT cells but not in AcP KO or AcPb KO cells. The ontology of electrically-induced ERPs was delayed in AcP KO cells. We conclude IL1 signaling has a critical role in the emergence of sleep-linked network behavior with AcP playing a dominant role in the slowing of development while AcPb enhances development rates of sleep-linked emergent network properties.

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Interleukin-1 receptor accessory protein (AcP) is required for normal development of neuronal/glial network emergent electrophysiological properties.

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The neuron-specific isoform of AcP, AcPb, is required for enhancement of delta wave power by interleukin-1.

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Results provide further support for a) interleukin-1’s involvement in sleep regulation b) that it enhances sleep via AcPb and c) that sleep is a property of mature neuronal/glial networks whether in vitro or in vivo.

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.

Design of Cultured Neuron Networks in vitro with Predefined Connectivity Using Asymmetric Microfluidic Channels

The architecture of neuron connectivity in brain networks is one of the basic mechanisms by which to organize and sustain a particular function of the brain circuitry. There are areas of the brain composed of well-organized layers of neurons connected by unidirectional synaptic connections (e.g., cortex, hippocampus). Re-engineering of the neural circuits with such a heterogeneous network structure in culture may uncover basic mechanisms of emergent information functions of these circuits. In this study, we present such a model designed with two subpopulations of primary hippocampal neurons (E18) with directed connectivity grown in a microfluidic device with asymmetric channels. We analysed and compared neurite growth in the microchannels with various shapes that promoted growth dominantly in one direction. We found an optimal geometric shape features of the microchannels in which the axons coupled two chambers with the neurons. The axons grew in the promoted direction and formed predefined connections during the first 6 days in vitro (DIV). The microfluidic devices were coupled with microelectrode arrays (MEAs) to confirm unidirectional spiking pattern propagation through the microchannels between two compartments. We found that, during culture development, the defined morphological and functional connectivity formed and was maintained for up to 25 DIV.

The Effects of Acute GABA Treatment on the Functional Connectivity and Network Topology of Cortical Cultures

γ-Aminobutyric acid (GABA) is an inhibitory transmitter, acting on receptor channels to reduce neuronal excitability in matured neural systems. However, electrophysiological responses of whole neuronal ensembles to the exposure to GABA are still unclear. We used micro-electrode arrays (MEAs) to study the effects of the increasing amount of GABA on functional network of cortical neural cultures. Then the recorded data were analyzed by the cross-covariance analysis and graph theory. Results showed that after the GABA treatment, the activity parameters of firing rate, bursting rate, bursting duration and network burst frequency in neural cultures decreased as expected. In addition, the functional connectivity also decreased in similarity, network density, and the size of the largest component. However, small-worldness was not found to be influenced by the acute GABA treatment. Our results support the position that using graph theory to evaluate the functional connectivity of neural cultures may enhance understanding of the pharmacological impact of neurotransmitters on neuronal networks.

Repeated stimulation of cultured networks of rat cortical neurons induces parallel memory traces

During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and small-scale computational models to study the effect of memory replay on the formation of memory traces. We show that input-deprived networks develop an activity⇔connectivity balance where dominant activity patterns support current connectivity. Electrical stimulation at one electrode disturbs this balance and induces connectivity changes. Intrinsic forces in recurrent networks lead to a new equilibrium with activity patterns that include the stimulus response. The new connectivity is no longer disrupted by this stimulus, indicating that networks memorize it. A different stimulus again induces connectivity changes upon first application but not subsequently, demonstrating the formation of a second memory trace. Returning to the first stimulus does not affect connectivity, indicating parallel storage of both traces. A computer model robustly reproduced experimental results, suggesting that spike-timing-dependent plasticity and short time depression suffice to store parallel memory traces, even in networks without particular circuitry constraints.

Interaction of electrically evoked activity with intrinsic dynamics of cultured cortical networks with and without functional fast GABAergic synaptic transmission

The modulation of neuronal activity by means of electrical stimulation is a successful therapeutic approach for patients suffering from a variety of central nervous system disorders. Prototypic networks formed by cultured cortical neurons represent an important model system to gain general insights in the input–output relationships of neuronal tissue. These networks undergo a multitude of developmental changes during their maturation, such as the excitatory–inhibitory shift of the neurotransmitter GABA. Very few studies have addressed how the output properties to a given stimulus change with ongoing development. Here, we investigate input–output relationships of cultured cortical networks by probing cultures with and without functional GABA_Aergic synaptic transmission with a set of stimulation paradigms at various stages of maturation. On the cellular level, low stimulation rates (<15 Hz) led to reliable neuronal responses; higher rates were increasingly ineffective. Similarly, on the network level, lowest stimulation rates (<0.1 Hz) lead to maximal output rates at all ages, indicating a network wide refractory period after each stimulus. In cultures aged 3 weeks and older, a gradual recovery of the network excitability within tens of milliseconds was in contrast to an abrupt recovery after about 5 s in cultures with absent GABA_Aergic synaptic transmission. In these GABA deficient cultures evoked responses were prolonged and had multiple discharges. Furthermore, the network excitability changed periodically, with a very slow spontaneous change of the overall network activity in the minute range, which was not observed in cultures with absent GABA_Aergic synaptic transmission. The electrically evoked activity of cultured cortical networks, therefore, is governed by at least two potentially interacting mechanisms: A refractory period in the order of a few seconds and a very slow GABA dependent oscillation of the network excitability.

Tumor necrosis factor enhances the sleep-like state and electrical stimulation induces a wake-like state in co-cultures of neurons and glia

We characterise sleep-like states in cultured neurons and glia during development in vitro as well as after electrical stimulation, the addition of tumor necrosis factor alpha (TNF), and the combination of TNF plus electrical stimulation. We also characterise optogenetic stimulation-induced ATP release and neuronal interleukin-1 and TNF expression in vitro demonstrating the activity dependence of these putative sleep-regulatory substances. Action potential (AP) burstiness, expressed as the burstiness index (BI), synchronization of slow electrical potentials between recording electrodes (SYN), and slow wave (SW) power (0.25-3.75 Hz) determined using fast Fourier analyses emerged as network properties, maturing after 2 weeks in culture. Homologous in vivo measures are used to characterise sleep. Electrical stimulation reduced the BI, SYN and SW power values during and/or after the stimulus period. One day later, homeostasis was evident from rebounds of SYN and SW power values to above baseline levels; the magnitude of the rebound was stimulus pattern-dependent. The addition of TNF enhanced BI, SYN and SW power values, suggesting the induction of a deeper sleep-like state. Electrical stimulation reversed these TNF effects, suggesting the network state was more wake-like. The day after TNF plus electrical stimulation, the changes in SYN and SW power values were dependent upon the stimulus patterns the cells received the day before. We conclude that sleep and wake states in cultured in vitro networks can be controlled and they share molecular regulatory mechanisms with local in vivo networks. Further, sleep is an activity-dependent emergent local network property.

In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using multielectrode arrays

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments.

A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro

To advance our understanding of the functioning of neuronal ensembles, systems are needed to enable simultaneous recording from a large number of individual neurons at high spatiotemporal resolution and good signal-to-noise ratio. Moreover, stimulation capability is highly desirable for investigating, for example, plasticity and learning processes. Here, we present a microelectrode array (MEA) system on a single CMOS die for in vitro recording and stimulation. The system incorporates 26,400 platinum electrodes, fabricated by in-house post-processing, over a large sensing area (3.85 × 2.10 mm²) with sub-cellular spatial resolution (pitch of 17.5 μm). Owing to an area and power efficient implementation, we were able to integrate 1024 readout channels on chip to record extracellular signals from a user-specified selection of electrodes. These channels feature noise values of 2.4 μV_rms in the action-potential band (300 Hz-10 kHz) and 5.4 μV_rms in the local-field-potential band (1 Hz-300 Hz), and provide programmable gain (up to 78 dB) to accommodate various biological preparations. Amplified and filtered signals are digitized by 10 bit parallel single-slope ADCs at 20 kSamples/s. The system also includes 32 stimulation units, which can elicit neural spikes through either current or voltage pulses. The chip consumes only 75 mW in total, which obviates the need of active cooling even for sensitive cell cultures.

Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns

Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.

A simple model of cortical culture growth: burst property dependence on network composition and activity

This paper describes large-scale simulations of growth, network formation, and behavior in cultures of dissociated cortical cells. A neuron model that incorporates synaptic facilitation/depression and neurite outgrowth/retraction was used to construct virtual cultures of 10,000 cells whose spiking behavior and evolution were investigated in closed-loop simulations. This approach allows us to perform detailed analysis of the effects of model parameters on burst shape and timing, their changes, and the interrelationship among these behaviors, gross network structure, and model parameters. We examined the effects of two parameters--network composition (fraction of excitatory cells) and neuron excitability (activity level corresponding to neurite outgrowth equilibrium)--on network structure and behavior. Our results suggest that much of the burst shape and timing observed in vitro can be explained by a model that includes only closed-loop neurite outgrowth and dynamic synapses; features such as LTP/LTD, random connectivity, long-distance connections, and detailed neurite topology are not necessary.

Controlling neural network responsiveness: tradeoffs and constraints

In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input-output relations and activity dependent dispersion of neuronal pair-wise correlations across the network.

Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro

Linking the structural connectivity of brain circuits to their cooperative dynamics and emergent functions is a central aim of neuroscience research. Graph theory has recently been applied to study the structure-function relationship of networks, where dynamical similarity of different nodes has been turned into a “static” functional connection. However, the capability of the brain to adapt, learn and process external stimuli requires a constant dynamical functional rewiring between circuitries and cell assemblies. Hence, we must capture the changes of network functional connectivity over time. Multi-electrode array data present a unique challenge within this framework. We study the dynamics of gamma oscillations in acute slices of the somatosensory cortex from juvenile mice recorded by planar multi-electrode arrays. Bursts of gamma oscillatory activity lasting a few hundred milliseconds could be initiated only by brief trains of electrical stimulations applied at the deepest cortical layers and simultaneously delivered at multiple locations. Local field potentials were used to study the spatio-temporal properties and the instantaneous synchronization profile of the gamma oscillatory activity, combined with current source density (CSD) analysis. Pair-wise differences in the oscillation phase were used to determine the presence of instantaneous synchronization between the different sites of the circuitry during the oscillatory period. Despite variation in the duration of the oscillatory response over successive trials, they showed a constant average power, suggesting that the rate of expenditure of energy during the gamma bursts is consistent across repeated stimulations. Within each gamma burst, the functional connectivity map reflected the columnar organization of the neocortex. Over successive trials, an apparently random rearrangement of the functional connectivity was observed, with a more stable columnar than horizontal organization. This work reveals new features of evoked gamma oscillations in developing cortex.

Critical role for inhibitory neurons in modulation of synaptic signaling in ex vivo neuronal networks

A number of laboratories have modeled aspects of synaptic plasticity using neuronal networks established on micro-electrode arrays. Such studies demonstrate that external stimulation can increase or hasten maturation of network signaling as evidenced an increase in complex bursts. Herein, we demonstrate that repetitive stimulation with a recorded synaptic signal was capable of increasing overall signaling, including the percentage of bursts, over a 5-day period, but that this increase was completely prevented by the presence of the GABAergic antagonist bicuculline. These findings demonstrate a critical role for inhibitory neurons in signal maturation following stimulation, which supports the purported role for inhibitory neuronal activity in long-term potentiation and learning in situ.

Transient epileptiform signaling during neuronal network development: regulation by external stimulation and bimodal GABAergic activity

A predominance of excitatory activity, with protracted appearance of inhibitory activity, accompanies cortical neuronal development. It is unclear whether or not inhibitory neuronal activity is solicited exclusively by excitatory neurons or whether the transient excitatory activity displayed by developing GABAergic neurons contributes to an excitatory threshold that fosters their conversion to inhibitory activity. We addressed this possibility by culturing murine embryonic neurons on multi-electrode arrays. A wave of individual 0.2-0.4 mV signals ("spikes") appeared between approx. 20-30 days in culture, then declined. A transient wave of high amplitude (>0.5 mV) epileptiform activity coincided with the developmental decline in spikes. Bursts (clusters of ≥3 low-amplitude spikes within 0.7s prior to returning to baseline) persisted following this decline. Addition of the GABAergic antagonist bicuculline initially had no effect on signaling, consistent with delayed development of GABAergic synapses. This was followed by a period in which bicuculline inhibited overall signaling, confirming that GABAergic neurons initially display excitatory activity in ex vivo networks. Following the transient developmental wave of epileptiform signaling, bicuculline induced a resurgence of epileptiform signaling, indicating that GABAergic neurons at this point displayed inhibitory activity. The appearance of transition after the developmental and decline of epileptiform activity, rather than immediately after the developmental decline in lower-amplitude spikes, suggests that the initial excitatory activity of GABAergic neurons contributes to their transition into inhibitory neurons, and that inhibitory GABAergic activity is essential for network development. Prior studies indicate that a minority (25%) of neurons in these cultures were GABAergic, suggesting that inhibitory neurons regulate multiple excitatory neurons. A similar robust increase in signaling following cessation of inhibitory activity in an artificial neural network containing 20% inhibitory neurons supported this conclusion. Even a minor perturbation in GABAergic function may therefore foster initiation and/or amplification of seizure activity, as well as perturbations in long-term potentiation.

Detection of trace processes in the networks of neurons cultured on microelectrode arrays

We compared the effects of electrical stimulation of primary dissociated neuronal cultures cultured on microelectrode arrays in terms on the expression of c-fos transcriptional factor that is involved in plastic reorganization in neurons. Dissociated hippocampal neurons cultured on multielectrode arrays were exposed to two stimulation protocols: high-frequency and low-frequency stimulations. Expression of c-fos was evaluated using immunofluorescence. Both high-frequency and low-frequency stimulations significantly increased c-fos expression in comparison with non-stimulated control. These findings indicate that c-fos expression can be induced in neuronal cell culture by different types of electrical stimulations and can be used for studying plasticity processes in microphysiological in vitro systems.

Recording from defined populations of retinal ganglion cells using a high-density CMOS-integrated microelectrode array with real-time switchable electrode selection

In order to understand how retinal circuits encode visual scenes, the neural activity of defined populations of retinal ganglion cells (RGCs) has to be investigated. Here we report on a method for stimulating, detecting, and subsequently targeting defined populations of RGCs. The possibility to select a distinct population of RGCs for extracellular recording enables the design of experiments that can increase our understanding of how these neurons extract precise spatio-temporal features from the visual scene, and how the brain interprets retinal signals. We used light stimulation to elicit a response from physiologically distinct types of RGCs and then utilized the dynamic-configurability capabilities of a microelectronics-based high-density microelectrode array (MEA) to record their synchronous action potentials. The layout characteristics of the MEA made it possible to stimulate and record from multiple, highly overlapping RGCs simultaneously without light-induced artifacts. The high-density of electrodes and the high signal-to-noise ratio of the MEA circuitry allowed for recording of the activity of each RGC on 14±7 electrodes. The spatial features of the electrical activity of each RGC greatly facilitated spike sorting. We were thus able to localize, identify and record from defined RGCs within a region of mouse retina. In addition, we stimulated and recorded from genetically modified RGCs to demonstrate the applicability of optogenetic methods, which introduces an additional feature to target a defined cell type. The developed methodologies can likewise be applied to other neuronal preparations including brain slices or cultured neurons.

Accelerated establishment of mature signaling patterns following stimulation of developing neuronal networks: "learning" versus "plasticity"

Neuronal networks established on micro-electrode arrays provide useful models for synaptic plasticity. Whether or not this represents a facet of learning is debated since ex vivo networks are deprived of organismal interaction with the environment. We compared developmental signaling of such networks with and without stimulation with a prerecorded synaptic signal from another mature culture as a model of sensory input. Unstimulated networks displayed a developmental increase in individual signals that eventually declined, yielding a pattern containing organized bursts of signaling. Minimal stimulation, to model the onset of sensory input hastened the onset of developmental signaling. However, the overall developmental pattern of stimulated networks, including the total number and type of signals as well as the length of this developmental period, was identical to that of unstimulated networks. One interpretation of these findings is that ongoing plasticity may be essential to establish an appropriate platform for learning once sensory input ensues.

Human neuroblastoma cultures for biorobotics

This paper introduces a new biorobotic system using human neuroblastoma cultures and centre of area learning for basic robotic guidance. Multielectrode Arrays Setups have been designed for direct culturing neural cells over silicon or glass substrates, providing the capability to stimulate and record simultaneously populations of neural cells. The main objective of this work will be to control a robot using this biological neuroprocessor and a new simple centre of area learning scheme. The final system could be applied for testing how chemicals affect the behaviour of the robot or to establish the basis for new hybrid optogenetic neuroprostheses based on stimulating optically genetic-modified neurons.

Stimulation with a low-amplitude, digitized synaptic signal to invoke robust activity within neuronal networks on multielectrode arrays

Multielectrode arrays (MEAs) are used for analysis of neuronal activity. Here we report two variations on commonly accepted techniques that increase the precision of extracellular electrical stimulation: (i) the use of a low-amplitude recorded spontaneous synaptic signal as a stimulus waveform and (ii) the use of a specific electrode within the array adjacent to the stimulus electrode as a hard-grounded stimulus signal return path. Both modifications remained compatible with manipulation of neuronal networks. In addition, localized stimulation with the low-amplitude synaptic signal allowed selective stimulation or inhibition of otherwise spontaneous signals. These findings indicate that minimizing the area of the culture impacted by external stimulation allows modulation of signaling patterns within subpopulations of neurons in culture. The simple modifications described herein may be useful for precise monitoring and manipulation of neuronal networks.

Phase-dependent effects of stimuli locked to oscillatory activity in cultured cortical networks

The archetypal activity pattern in cultures of dissociated neurons is spontaneous network-wide bursting. Bursts may interfere with controlled activation of synaptic plasticity, but can be suppressed by the application of stimuli at a sufficient rate. We sinusoidally modulated (4 Hz) the pulse rate of random background stimulation (RBS) and found that cultures were more active, burst less frequently, and expressed oscillatory activity. Next, we studied the effect of phase-locked tetani (four pulses, 200 s(-1)) on network activity. Tetani were applied to one electrode at the peak or trough of mRBS stimulation. We found that when tetani were applied at the peak of modulated RBS (mRBS), a significant potentiation of poststimulus histograms (PSTHs) occurred. Conversely, tetani applied at the trough resulted in a small but insignificant depression of PSTHs. In addition to PSTHs, electrode-specific firing rate profiles within spontaneous bursts before and after mRBS were analyzed. Here, significant changes in firing rate profiles were found only for stimulation at the peak of mRBS. Our study shows that rhythmic activity in culture is possible, and that the network responds differentially to strong stimuli depending on the phase at which they are delivered. This suggests that plasticity mechanisms may be differentially accessible in an oscillatory state.

Connecting neurons to a mobile robot: an in vitro bidirectional neural interface

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

The effect of slow electrical stimuli to achieve learning in cultured networks of rat cortical neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.

Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these "connectivity methods" on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.

Latency-related development of functional connections in cultured cortical networks

To study plasticity, we cultured cortical networks on multielectrode arrays, enabling simultaneous recording from multiple neurons. We used conditional firing probabilities to describe functional network connections by their strength and latency. These are abstract representations of neuronal pathways and may arise from direct pathways between two neurons or from a common input. Functional connections based on direct pathways should reflect synaptic properties. Therefore, we searched for long-term potentiation (this mechanism occurs in vivo when presynaptic action potentials precede postsynaptic ones with interspike intervals up to approximately 20 ms) in vitro. To investigate if the strength of functional connections showed a similar latency-related development, we selected periods of monotonously increasing or decreasing strength. We observed increased incidence of short latencies (5-30 ms) during strengthening, whereas these rarely occurred during weakening. Furthermore, we saw an increased incidence of 40-65 ms latencies during weakening. Conversely, functional connections tended to strengthen in periods with short latency, whereas strengthening was significantly less than average during long latency. Our data suggest that functional connections contain information about synaptic connections, that conditional firing probability analysis is sensitive enough to detect it and that a substantial fraction of all functional connections is based on direct pathways.

Statistical long-term correlations in dissociated cortical neuron recordings

The study of nonlinear long-term correlations in neuronal signals is a central topic for advanced neural signal processing. In particular, the existence of long-term correlations in neural signals recorded via multielectrode array (MEA) could provide interesting information about changes in interneuron communications. In this study we propose a new method for long-term correlation analysis of neuronal burst activity based on the periodogram alpha slope estimation of the MEA signal. We applied our method to recordings taken from cultured networks of dissociated rat cortical neurons. We show the effectiveness of the method in analyzing the activity changes as well as the temporal dynamics that take place during the development of such cultures. Results demonstrate that the alpha parameter is able to divide the network development in three well-defined stages, showing pronounced variations in the long-term correlation among bursts.

The estimation of long-term memory characteristics in MEA neuronal culture recordings

The nonlinear analysis of multichannel MEA recordings from neuronal networks is becoming a central topic in Neuroengineering. Up-to-date these kind of analyses required complex ad hoc methods. In this paper we introduce a new approach that allows the analysis of the whole-neuronal-network-activity with well-established nonlinear signal processing methods. In particular, we show here the estimation of long-term-memory behaviors through the Periodogram method in the bursting activity of cortical neuron cultures during development. Moreover, we show how this method is able to highlight structural activity changes of the network.

A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals

The spike represents the fundamental bit of information transmitted by the neurons within a network in order to communicate. Then, given the importance of the spike rate as well as the spike time for coding the activity generated at the level of a cell assembly, a relevant issue in extracellular electrophysiology is the correct identification of the spike in multisite recordings from brain areas or neuronal networks. In this paper, we present a novel spike detection algorithm, named Precise Timing Spike Detection (PTSD), aimed at (i) reducing the number of false positives and false negatives, in order to optimize the rate code, and (ii) improving the time precision of the identified spike, in order to optimize the spike timing. The PTSD algorithm considers consecutive portions of the signal and looks for the Relative Maximum/Minimum whose peak-to-peak amplitude is above a defined differential threshold and responds to specific requirements. To validate the algorithm, the presented spike detection has been compared with other methods either commercially available or proposed in the literature by using two benchmarking procedures: (i) visual inspection by a group of experts of a portion of signal recorded from a rat cortical culture and (ii) detection of the spikes generated by a realistic neuronal network model. In both cases our algorithm produced the best performances in terms of efficiency and precision. The ROC curve analysis further proved that the best results are reached by the application of the PTSD.

Liquid state machines and cultured cortical networks: the separation property

In vitro neural networks of cortical neurons interfaced to a computer via multichannel microelectrode arrays (MEA) provide a unique paradigm to create a hybrid neural computer. Unfortunately, only rudimentary information about these in vitro network's computational properties or the extent of their abilities are known. To study those properties, a liquid state machine (LSM) approach was employed in which the liquid (typically an artificial neural network) was replaced with a living cortical network and the input and readout functions were replaced by the MEA-computer interface. A key requirement of the LSM architecture is that inputs into the liquid state must result in separable outputs based on the liquid's response (separation property). In this paper, high and low frequency multi-site stimulation patterns were applied to the living cortical networks. Two template-based classifiers, one based on Euclidean distance and a second based on a cross-correlation were then applied to measure the separation of the input-output relationship. The result was over a 95% (99.8% when nonstationarity is compensated) input reconstruction accuracy for the high and low frequency patterns, confirming the existence of the separation property in these biological networks.