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In the last decades, planar multi-electrode arrays (MEAs) have been widely used to record activity from in vitro neuronal cell cultures and tissue slices. Though successful, this technique bears some limitations, particularly relevant when applied to three-dimensional (3D) tissue, such as brain slices, spheroids or organoids. For example, planar MEAs signals are informative on just one side of a 3D-organized structure. This limits the interpretation of the results in terms of network functions in a complex structured and hyperconnected brain tissue. Moreover, the side in contact with the MEAs often shows lower oxygenation rates and related vitality issues. To overcome these problems, we empowered a CMOS high-density multi-electrode array (HD-MEA) with thousands of microneedles (μneedles) of 65-90 μm height, able to penetrate and record in-tissue signals, providing for the first time a 3D HD-MEA chip. We propose a CMOS-compatible fabrication process to produce arrays of μneedles of different widths mounted on large pedestals to create microchannels underneath the tissue. By using cerebellar and cortico-hippocampal slices as a model, we show that the μneedles efficiently penetrate the 3D tissue while the microchannels allow the flowing of maintenance solutions to increase tissue vitality in the recording sites. These improvements are reflected by the increase in electrodes sensing capabilities, the number of sampled neuronal units (compared to matched planar technology), and the efficiency of compound effects. Importantly, each electrode can also be used to stimulate the tissue with optimal efficiency due to the 3D structure. Furthermore, we demonstrate how the 3D HD-MEA can efficiently penetrate and get outstanding signals from in vitro 3D cellular models as brain spheroids. In conclusion, we describe a new recording device characterized by the highest spatio-temporal resolution reported for a 3D MEA and significant improvements in the quality of recordings, with a high signal-to-noise ratio and improved tissue vitality. The applications of this game-changing technique are countless, opening unprecedented possibilities in the neuroscience field and beyond.

Nanoparticles (NPs) are increasingly used in biomedical applications, but the factors that influence their interactions with living cells need to be elucidated. Here, wereveal the role of NP surface charge in determining theirneuronal interactions and electrical responses. We discoveredthat negatively charged NPs administered at low concentration(10 nM) interact with the neuronal membrane and at the synaptic cleft, whereas positively and neutrally charged NPs never localize on neurons. This effect is shape and material independent. The presence of negatively charged NPs on neuronal cell membranes influences the excitability of neurons by causing an increase in the amplitude and frequency of spontaneous postsynaptic currents at the single cell level and an increase of both the spiking activity and synchronous firing at neural network level. The negatively charged NPs exclusively bind to excitable neuronal cells, and never to nonexcitable glial cells. This specific interaction was also confirmed by manipulating the electrophysiological activity of neuronal cells. Indeed, the interaction of negatively charged NPs with neurons is either promoted or hindered by pharmacological suppression or enhancement of the neuronal activity with tetrodotoxin or bicuculline, respectively. We further support our main experimental conclusions by using numerical simulations. This study demonstrates that negatively charged NPs modulate the excitability of neurons, revealing the potential use of NPs for controlling neuron activity.

The control of neuron-substrate adhesion has been always a challenge for fabricating neuron-based cell chips and in particular for multielectrode array (MEA) devices, which warrants the investigation of the electrophysiological activity of neuronal networks. The recent introduction of high-density chips based on the complementary metal oxide semiconductor (CMOS) technology, integrating thousands of electrodes, improved the possibility to sense large networks and raised the challenge to develop newly adapted functionalization techniques to further increase neuron electrode localization to avoid the positioning of cells out of the recording area. Here, we present a simple and straightforward chemical functionalization method that leads to the precise and exclusive positioning of the neural cell bodies onto modified electrodes and inhibits, at the same time, cellular adhesion in the surrounding insulator areas. Different from other approaches, this technique does not require any adhesion molecule as well as complex patterning technique such as μ-contact printing. The functionalization was first optimized on gold (Au) and silicon nitride (Si3N4)-patterned surfaces. The procedure consisted of the introduction of a passivating layer of hydrophobic silane molecules (propyltriethoxysilane [PTES]) followed by a treatment of the Au surface using 11-amino-1-undecanethiol hydrochloride (AT). On model substrates, well-ordered neural networks and an optimal coupling between a single neuron and single micrometric functionalized Au surface were achieved. In addition, we presented the preliminary results of this functionalization method directly applied on a CMOS-MEA: the electrical spontaneous spiking and bursting activities of the network recorded for up to 4 weeks demonstrate an excellent and stable neural adhesion and functional behavior comparable with what expected using a standard adhesion factor, such as polylysine or laminin, thus demonstrating that this procedure can be considered a good starting point to develop alternatives to the traditional chip coatings to provide selective and specific neuron-substrate adhesion.

Electrophysiological signals in the brain are distributed over broad spatial and temporal scales. Monitoring these signals at multiple scales is fundamental in order to decipher how brain circuits operate and might dysfunction in disease. A possible strategy to enlarge the experimentally accessible spatial and temporal scales consists in combining the use of multiple probes with different resolutions and sensing areas. Here, we propose a neural recording system capable of simultaneous and synchronous acquisitions from a new generation of high-resolution CMOS probes (512 microelectrodes, 25 kHz/electrode whole-array sampling frequency) as well as from a custom-designed CMOS-based headstage. While CMOS probes can provide recordings from a large number of closely spaced electrodes on single-shaft devices, the CMOS-based headstage can be used to interface the wide range of available intra- or epi-cortical passive electrode array devices. The current platform was designed to simultaneously manage high-resolution recordings from up to four differently located CMOS probes and from a single 36-channels low-resolution passive electrode array device. The design, implementation, and performances for both ICs and for the FPGA-based interface are presented. Experiments on retina and neuronal culture preparations demonstrate the recording of neural spiking activity for both CMOS devices and the functionality of the system.

Substrate-integrated multielectrode arrays (MEAs) enable multisite, long-term, and label-free sensing and actuation of neuronal electrical signals in reduced cell culture models for network electrophysiology. Conventional, thin-film fabricated passive MEAs typically provide a few tens of electrode sites. New generations of active CMOS-based high-resolution arrays provide the capabilities of simultaneous recordings from thousands of neurons over fields of view of several square millimeters, yet allowing extracellular electrical imaging to be achieved down to the subcellular scale. In turn, such advancement in chip-based electrical readouts can significantly complement recently developed biotechnological and bimolecular techniques for neurobiology applications. Here, we describe (1) a simple method to fabricate passive MEAs and (2) protocols for preparing and growing primary rat hippocampal neuronal cultures and human iPS-derived neurons on MEAs. The aim is to provide reliable protocols for initiating the reader to this technology and for stimulating their further development and experimental use in neurobiology.

Substrate-integrated multielectrode arrays (MEAs) enable multisite, long-term, and label-free sensing and actuation of neuronal electrical signals in reduced cell culture models for network electrophysiology. Conventional, thin-film fabricated passive MEAs typically provide a few tens of electrode sites. New generations of active CMOS-based high-resolution arrays provide the capabilities of simultaneous recordings from thousands of neurons over fields of view of several square millimeters, yet allowing extracellular electrical imaging to be achieved down to the subcellular scale. In turn, such advancement in chip-based electrical readouts can significantly complement recently developed biotechnological and bimolecular techniques for neurobiology applications. Here, we describe (1) a simple method to fabricate passive MEAs and (2) protocols for preparing and growing primary rat hippocampal neuronal cultures and human iPS-derived neurons on MEAs. The aim is to provide reliable protocols for initiating the reader to this technology and for stimulating their further development and experimental use in neurobiology.

In this work we present a FPGA-based system for real-time processing of neural signals acquired by commercial high-density microelectrode array (HDMEA). The considered MEA features 4096 electrodes with 18kHz sampling frequency and 12-bit resolution, thus produces nearly 1 Gbps of data. Within the implementation, we considered low-latency as a main objective, to allow for closed-loop acquisition-stimulation experiments, that represent a novel promising frontier in neuro-physiology and in the development of brain-machine interfaces. The developed platform is implemented on a low-to-mid Zynq all-programmable SoC, and is able to perform all the required computation (from signal acquisition to response generation) with less than 2ms latency, enabling closed-loop applications in a wide range of experiments.

Understanding the interplay among the spectrum of electrophysiological signals in the brain, distributed over broad spatial and temporal scales, is fundamental to decipher how brain circuits operate and might dysfunction in disease. Here, we present a multiscale recording system for simultaneous and synchronous acquisitions from a new generation of im-plantable CMOS active probes (single-shaft, 512 microelectrodes, 25 kHz/electrode full-array sampling) as well as from implantable conventional electrode array probes interfaced with a custom-designed CMOS-based headstage. The platform can manage highresolution recordings from up to 4 differently located probes, and one 36-channels low-resolution passive electrode array. As presented here, and prior to complete in-vivo validation, the design and recording performance of high- and low-density electrode array ICs were experimentally tested ex-vivo on retinal whole-mounts.

The immature retina generates spontaneous waves of spiking activity that sweep across the ganglion cell layer during a limited period of development before the onset of visual experience. The spatiotemporal patterns encoded in the waves are believed to be instructive for the wiring of functional connections throughout the visual system. However, the ontogeny of retinal waves is still poorly documented as a result of the relatively low resolution of conventional recording techniques. Here, we characterize the spatiotemporal features of mouse retinal waves from birth until eye opening in unprecedented detail using a large-scale, dense, 4096-channel multielectrode array that allowed us to record from the entire neonatal retina at near cellular resolution. We found that early cholinergic waves propagate with random trajectories over large areas with low ganglion cell recruitment. They become slower, smaller and denser when GABAA signalling matures, as occurs beyond postnatal day (P) 7. Glutamatergic influences dominate from P10, coinciding with profound changes in activity dynamics. At this time, waves cease to be random and begin to show repetitive trajectories confined to a few localized hotspots. These hotspots gradually tile the retina with time, and disappear after eye opening. Our observations demonstrate that retinal waves undergo major spatiotemporal changes during ontogeny. Our results support the hypotheses that cholinergic waves guide the refinement of retinal targets and that glutamatergic waves may also support the wiring of retinal receptive fields.

Neurotoxicity and the accumulation of extracellular amyloid-beta1–42 (Aβ) peptides are associated with the development of Alzheimer’s disease (AD) and correlate with neuronal activity and network dysfunctions, ultimately leading to cellular death. However, research on neurodegenerative diseases is hampered by the paucity of reliable readouts and experimental models to study such functional decline from an early onset and to test rescue strategies within networks at cellular resolution. To overcome this important obstacle, we demonstrate a simple yet powerful in vitro AD model based on a rat hippocampal cell culture system that exploits large-scale neuronal recordings from 4096-electrodes on CMOS-chips for electrophysiological quantifications. This model allows us to monitor network activity changes at the cellular level and to uniquely uncover the early activity-dependent deterioration induced by Aβ-neurotoxicity. We also demonstrate the potential of this in vitro model to test a plausible hypothesis underlying the Aβ-neurotoxicity and to assay potential therapeutic approaches. Specifically, by quantifying N-methyl D-aspartate (NMDA) concentration-dependent effects in comparison with low-concentration allogenic-Aβ, we confirm the role of extrasynaptic-NMDA receptors activation that may contribute to Aβ-neurotoxicity. Finally, we assess the potential rescue of neural stem cells (NSCs) and of two pharmacotherapies, memantine and saffron, for reversing Aβ-neurotoxicity and rescuing network-wide firing.

The recent availability of human induced pluripotent stem cells (hiPSCs) holds great promise as a novel source of human-derived neurons for cell and tissue therapies as well as for in vitro drug screenings that might replace the use of animal models. However, there is still a considerable lack of knowledge on the functional properties of hiPSC-derived neuronal networks, thus limiting their application. Here, upon optimization of cell culture protocols, we demonstrate that both spontaneous and evoked electrical spiking activities of these networks can be characterized on-chip by taking advantage of the resolution provided by CMOS multielectrode arrays (CMOS-MEAs). These devices feature a large and closely-spaced array of 4096 simultaneously recording electrodes and multi-site on-chip electrical stimulation. Our results show that networks of human-derived neurons can respond to electrical stimulation with a physiological repertoire of spike waveforms after three months of cell culture, a period of time during which the network undergoes the expression of developing patterns of spontaneous spiking activity. To achieve this, we have investigated the impact on the network formation and on the emerging network-wide functional properties induced by different biochemical substrates, i.e. poly-dl-ornithine (PDLO), poly-l-ornithine (PLO), and polyethylenimine (PEI), that were used as adhesion promoters for the cell culture. Interestingly, we found that neuronal networks grown on PDLO coated substrates show significantly higher spontaneous firing activity, reliable responses to low-frequency electrical stimuli, and an appropriate level of PSD-95 that may denote a physiological neuronal maturation profile and synapse stabilization. However, our results also suggest that even three-month culture might not be sufficient for human-derived neuronal network maturation. Taken together, our results highlight the tight relationship existing between substrate coatings and emerging network properties, i.e. spontaneous activity, responsiveness, synapse formation and maturation. Additionally, our results provide a baseline on the functional properties expressed over three months of network development for a commercially available line of hiPSC-derived neurons. This is a first step toward the development of functional pre-clinical assays to test pharmaceutical compounds on human-derived neuronal networks with CMOS-MEAs.

We used electrophysiological signals recorded by CMOS Micro Electrode Arrays (MEAs) at high spatial resolution to estimate the functional-effective connectivity of sparse hippocampal neuronal networks in vitro by applying a cross-correlation (CC) based method and ad hoc developed spatio-temporal filtering. Low-density cultures were recorded by a recently introduced CMOS-MEA device providing simultaneous multi-site acquisition at high-spatial (21 μm inter-electrode separation) as well as high-temporal resolution (8 kHz per channel). The method is applied to estimate functional connections in different cultures and it is refined by applying spatio-temporal filters that allow pruning of those functional connections not compatible with signal propagation. This approach permits to discriminate between possible causal influence and spurious co-activation, and to obtain detailed maps down to cellular resolution. Further, a thorough analysis of the links strength and time delays (i.e., amplitude and peak position of the CC function) allows characterizing the inferred interconnected networks and supports a possible discrimination of fast mono-synaptic propagations, and slow poly-synaptic pathways. By focusing on specific regions of interest we could observe and analyze microcircuits involving connections among a few cells. Finally, the use of the high-density MEA with low density cultures analyzed with the proposed approach enables to compare the inferred effective links with the network structure obtained by staining procedures.

Recent developments in the neuroengineering field and the widespread use of the micro electrode arrays (MEAs) for electrophysiological investigations made available new approaches for studying the dynamics of dissociated neuronal networks as well as acute/organotypic slices maintained ex vivo. Importantly, the extraction of relevant parameters from these neural populations is likely to involve long-term measurements, lasting from a few hours to entire days. The processing of huge amounts of electrophysiological data, in terms of computational time and automation of the procedures, is actually one of the major bottlenecks for both in vivo and in vitro recordings. In this paper we present a collection of algorithms implemented within a new software package, named the Neural Signal Manager (NSM), aimed at analyzing a huge quantity of data recorded by means of MEAs in a fast and efficient way. The NSM offers different approaches for both spike and burst analysis, and integrates state-of-the-art statistical algorithms, such as the inter-spike interval histogram or the post stimulus time histogram, with some recent ones, such as the burst detection and its related statistics. In order to show the potentialities of the software, the application of the developed algorithms to a set of spontaneous activity recordings from dissociated cultures at different ages is presented in the Results section. Copyright © 2008 John Wiley & Sons, Ltd.

The microelectrode array (MEA) technology is continuously progressing towards higher integration of an increasing number of electrodes. The ensuing data streams that can be of several hundreds or thousands of Megabits/s require the implementation of new signal processing and data handling methodologies to substitute the currently used off-line analysis methods. Here, we present one approach based on the hardware implementation of a wavelet-based solution for real-time processing of extracellular neuronal signals acquired on high-density MEAs. We demonstrate that simple mathematical operations on the discrete wavelet transform (DWT) coefficients can be used for efficient neuronal spike detection and sorting. As the DWT is particularly well suited for implementation on dedicated hardware, we elaborated a wavelet processor on a field programmable gate array (FPGA) in order to compute the wavelet coefficients on 256 channels in real-time. By providing sufficient hardware resources, this solution can be easily scaled up for processing more electrode channels. Copyright © 2008 John Wiley & Sons, Ltd.

Visual Abstract, How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of a RGC pair encode spatial information. Thus, a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently., How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of a RGC pair encode spatial information. Thus, a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. Here, we tested this hypothesis in mouse by recording simultaneous light-evoked responses from hundreds of RGCs, at pan-retinal level, using a new generation of large-scale, high-density multielectrode array consisting of 4096 electrodes. Interestingly, we did not find any RGCs exhibiting a clear latency tuning to the stimuli, suggesting that in mouse, individual RGC pairs may not provide sufficient information. We show that a significant amount of information is encoded synergistically in the concerted spiking of large RGC populations. Thus, the RGC population response described with relative activities, or ranks, provides more relevant information than classical independent spike count- or latency- based codes. In particular, we report for the first time that when considering the relative activities across the whole population, the wave of first stimulus-evoked spikes is an accurate indicator of stimulus content. We show that this coding strategy coexists with classical neural codes, and that it is more efficient and faster. Overall, these novel observations suggest that already at the level of the retina, concerted spiking provides a reliable and fast strategy to rapidly transmit new visual scenes.

High density multielectrode arrays (MEAs) based on CMOS technology (CMOS-MEAs) can simultaneously record extracellular spiking activity in neuronal cultures from 4096 closely spaced microelectrodes. This allows for a finer investigation of neuronal network activity compared to conventional MEAs with a few tens of electrodes. However, the sensing properties of these devices differ. To highlight this aspect, here we investigate and discuss the differences observed when quantifying spontaneous synchronized bursting events (SBEs) in datasets acquired with conventional MEAs and high-density MEAs from comparable hippocampal cultures. We found that datasets acquired with high-density MEAs exhibit collective dynamics similar to conventional arrays, but are characterized by a higher percentage of random spikes, i.e. spikes that are not part of a burst, most probably resulting from the larger recording capability. Additionally, the percentage of electrodes that record a burst is remarkably small on high-density MEAs compared to what can be observed on conventional MEAs and SBEs appear to be propagating in time across the electrode array, by involving shorter sequences of spikes per electrode. Overall, these results highlight a lower level of network synchronization involved in SBEs compared to what has been debated for several decades based on conventional MEA recordings from cell cultures.

Deciphering neural network function in health and disease requires recording from many active neurons simultaneously. Developing approaches to increase their numbers is a major neurotechnological challenge. Parallel to recent advances in optical Ca(2+) imaging, an emerging approach consists in adopting complementary-metal-oxide-semiconductor (CMOS) technology to realize MultiElectrode Array (MEA) devices. By implementing signal conditioning and multiplexing circuits, these devices allow nowadays to record from several thousands of single neurons at sub-millisecond temporal resolution. At the same time, these recordings generate very large data streams which become challenging to analyze. Here, at first we shortly review the major approaches developed for data management and analysis for conventional, low-resolution MEAs. We highlight how conventional computational tools cannot be easily up-scaled to very large electrode array recordings, and custom bioinformatics tools are an emerging need in this field. We then introduce a novel approach adapted for the acquisition, compression and analysis of extracellular signals acquired simultaneously from 4096 electrodes with CMOS MEAs. Finally, as a case study, we describe how this novel large scale recording platform was used to record and analyze extracellular spikes from the ganglion cell layer in the wholemount retina at pan-retinal scale following patterned light stimulation.

Large scale CMOS-MEAs are an emerging neurotechnology enabling extracellular recordings in-vitro and in-vivo with thousand's electrodes simultaneously. This is on the way to provide the unprecedented capability of acquiring signals from several thousands of single-units, thus opening novel perspectives for electrophysiology, but also novel challenges for analysis and management of large datasets. Here, we propose an analysis platform designed for managing unprecedentedly large datasets of electrical recordings acquired with a 4096-electrode array platform. Furthermore it provides a computational framework to facilitate the development and integration of new analysis tools exploiting high-resolution electrical recordings.

Neurons communicate in the brain via spikes. Understanding the balance between the fast-firing minority and slow-firing majority in a neuronal population is therefore a fundamental step to unravel the nature of communication and of operation within and across neuronal assemblies. Recent in vivo observations show that many functional and structural parameters of the brain follow a skewed nature and typically manifest lognormal distributions of patterns. Here, we show for the first time that high-density microelectrode array (HD-MEA) reveal such a lognormal-like distribution of the firing patterns also in in vitro grown hippocampal neuronal networks, and already after 10 minutes of recording. Additionally, we demonstrate that the electrode density plays a key role for obtaining such a distribution in cultures. Overall, our findings show that in vitro neural networks recorded with CMOS-MEAs might contribute in investigating the organization and function of neuronal networks by revealing, with similarities with results obtained in vivo, the relationships among different skewed distributions at multiple scales, i.e. from synapses, single cells and micro-circuits up to large-scale networks.

Neuronal signaling in brain circuits occurs at multiple scales ranging from molecules and cells to large neuronal assemblies. However, current sensing neurotechnologies are not designed for parallel access of signals at multiple scales. With the aim of combining nanoscale molecular sensing with electrical neural activity recordings within large neuronal assemblies, in this work three-dimensional (3D) plasmonic nanoantennas are integrated with multielectrode arrays (MEA). Nanoantennas are fabricated by fast ion beam milling on optical resist; gold is deposited on the nanoantennas in order to connect them electrically to the MEA microelectrodes and to obtain plasmonic behavior. The optical properties of these 3D nanostructures are studied through finite elements method (FEM) simulations that show a high electromagnetic field enhancement. This plasmonic enhancement is confirmed by surface enhancement Raman spectroscopy of a dye performed in liquid, which presents an enhancement of almost 100 times the incident field amplitude at resonant excitation. Finally, the reported MEA devices are tested on cultured rat hippocampal neurons. Neurons develop by extending branches on the nanostructured electrodes and extracellular action potentials are recorded over multiple days in vitro. Raman spectra of living neurons cultured on the nanoantennas are also acquired. These results highlight that these nanostructures could be potential candidates for combining electrophysiological measures of large networks with simultaneous spectroscopic investigations at the molecular level.

Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity., Author summary Coordinated spontaneous spiking activity is fundamental for the normal formation of brain circuits during development. However, how ensembles of neurons generate these events remains unclear. To address this question, in the present study, we investigated the network properties that might be required to a neuronal system for the generation of these spontaneous waves of spikes. We performed our study on spontaneously active neuronal cell cultures using high-resolution electrical recordings and a computational network model developed to reproduce our experimental data both quantitatively and qualitatively. Through the analysis of both experimental and simulated data, we found that network bursts are initiated in regions of the network, or “functional communities”, characterized by particular local connectivity properties. We also found that these regions can amplify the background asynchronous spiking activity preceding a network burst and, in this way, can give rise to coordinated spiking events. As a whole, our results suggest the presence of functional communities of neurons in a developing neuronal system that might naturally emerge by following simple constraints on distance-based connectivity. These regions are most likely required for the generation of the spontaneous coordinated activity that can drive activity-dependent circuit formation.

Recent progresses in neuroelectronics and lab-on-a-chip technologies are providing novel opportunities for neuroscience research and applications. However, the experimental performances of these novel devices are not only the result of the artificially implemented features, such as those resulting from advanced electrode materials, from electrode morphologies, or from the low noise levels of the front-end electronic circuits. Rather, these performances also strictly relay on the bioartificial interface established by neurons on these devices. Here, we focus on cell culture systems adapted to neuroelectronic devices that were developed for organizing and growing neural networks in two or three dimensions. These developments span the fields of biosensors, engineering, neuroscience, and novel nanostructures and materials. Additionally, they are at the origin of novel neuroartificial hybrid technologies that can be applied for the study of neuronal networks at unprecedented scales and for applications in neuroscience that use scaffolding micro-/nanostructures, neurons, and biomolecules for advanced neuroelectronic interfaces and novel cell culture systems.

We propose the development of an innovative plasmonic-electronic multifunctional platform, capable at the same time of performing chemical analysis and electronic recordings from a cellular interface. The system, based on 3D hollow metallic nanotubes, integrated on customized multi-electrode-arrays, allows the study of neuronal signaling over different lengths, spanning from the molecular, to the cellular, to the network scale. Here we show that the same structures are efficient electric field enhancers, despite the continuous metal layer at the base, which connects them to the electric components of the integrated circuits. The methodology we propose, due to its simplicity and high throughput, has the potential for further improvements both in the field of plasmonics, and in the integration on large areas of commercial active electronic devices.

Despite many structural and functional aspects of the brain organization have been extensively studied in neuroscience, we are still far from a clear understanding of the intricate structure-function interactions occurring in the multi-layered brain architecture, where billions of different neurons are involved. Although structure and function can individually convey a large amount of information, only a combined study of these two aspects can probably shade light on how brain circuits develop and operate at the cellular scale. Here, we propose a novel approach for refining functional connectivity estimates within neuronal networks using the structural connectivity as prior. This is done at the mesoscale, dealing with thousands of neurons while reaching, at the microscale, an unprecedented cellular resolution. The High-Density Micro Electrode Array (HD-MEA) technology, combined with fluorescence microscopy, offers the unique opportunity to acquire structural and functional data from large neuronal cultures approaching the granularity of the single cell. In this work, an advanced method based on probabilistic directional features and heat propagation is introduced to estimate the structural connectivity from the fluorescence image while functional connectivity graphs are obtained from the cross-correlation analysis of the spiking activity. Structural and functional information are then integrated by reweighting the functional connectivity graph based on the structural prior. Results show that the resulting functional connectivity estimates are more coherent with the network topology, as compared to standard measures purely based on cross-correlations and spatio-temporal filters. We finally use the obtained results to gain some insights on which features of the functional activity are more relevant to characterize actual neuronal interactions.

The ultimate goal of neuroscience is understanding the brain at a functional level. This requires the investigation of the structural connectivity at multiple scales: from the single-neuron micro-connectomics to the brain-region macro-connectomics. In this work, we address the study of connectomics at the intermediate mesoscale, introducing a probabilistic approach capable of reconstructing complex topologies of large neuronal networks. Suitable directional features are designed to model the local neuritic architecture and a feature-based particle filtering framework is proposed which allows the spatial tracking of neurites on microscopy images. The experimental results on cultures of increasing complexity, grown on High-Density Micro Electrode Arrays, show good stability and performance as compared to ground truth annotations drawn by domain experts. We also show how the method can be used to dissect the structural connectivity of inhibitory and excitatory subnetworks opening new perspectives towards the investigation of functional interactions among multiple cellular populations.

The central nervous system of mammals is among the most elaborate structures in nature. For example, the cerebral cortex, which is involved in perception, motor control, attention, and memory, is organized in horizontal layers, each of astonishing complexity (Jones and Peters 1990). One cubic millimeter of mammalian neocortex contains about 100,000 neurons (Meyer et al. 2010). Each neuron receives on the order of 20,000 synapses and communicates with tens to hundreds of other cells in an extraordinarily complex and highly interwoven cellular network. Moreover, neurons are remarkably diverse in terms of their morphology, electrical properties, connectivity, and neurotransmitter phenotype.

Among the different methodologies used for electrophysiological measures in the brain, electrodes have played an undisputed role in high-quality intracellular signal recordings from a few neurons and in chronic extracellular measures with electrode-array probes implanted in the brain. Electrode arrays providing multisite extracellular measures have become a key methodology in neuroscience for studying coding and transmission of information by neuronal ensembles [1] and for the development of Brain–Machine Interfaces (BMIs) and neural prosthetics [2–8]. This is mainly because electrode arrays combine the unique features of bidirectionality (i.e., recording and stimulation), long-term stability (up to years), and of a large signal bandwidth that enables recordings of action potentials from multiple neurons as well as low-frequency field potentials (LFPs).

Recent progresses in neuroelectronics and lab-on-a-chip technologies are providing novel opportunities for neuroscience research and applications. However, the experimental performances of these novel devices are not only the result of the artificially implemented features, such as those resulting from advanced electrode materials, from electrode morphologies, or from the low noise levels of the front-end electronic circuits. Rather, these performances also strictly relay on the bioartificial interface established by neurons on these devices. Here, we focus on cell culture systems adapted to neuroelectronic devices that were developed for organizing and growing neural networks in two or three dimensions. These developments span the fields of biosensors, engineering, neuroscience, and novel nanostructures and materials. Additionally, they are at the origin of novel neuroartificial hybrid technologies that can be applied for the study of neuronal networks at unprecedented scales and for applications in neuroscience that use scaffolding micro-/nanostructures, neurons, and biomolecules for advanced neuroelectronic interfaces and novel cell culture systems.

The immature retina generates spontaneous waves of spiking activity that sweep across the ganglion cell layer during a limited period of development before the onset of visual experience. The spatiotemporal patterns encoded in the waves are believed to be instructive for the wiring of functional connections throughout the visual system. However, the ontogeny of retinal waves is still poorly documented as a result of the relatively low resolution of conventional recording techniques. Here, we characterize the spatiotemporal features of mouse retinal waves from birth until eye opening in unprecedented detail using a large-scale, dense, 4096-channel multielectrode array that allowed us to record from the entire neonatal retina at near cellular resolution. We found that early cholinergic waves propagate with random trajectories over large areas with low ganglion cell recruitment. They become slower, smaller and denser when GABAA signalling matures, as occurs beyond postnatal day (P) 7. Glutamatergic influences dominate from P10, coinciding with profound changes in activity dynamics. At this time, waves cease to be random and begin to show repetitive trajectories confined to a few localized hotspots. These hotspots gradually tile the retina with time, and disappear after eye opening. Our observations demonstrate that retinal waves undergo major spatiotemporal changes during ontogeny. Our results support the hypotheses that cholinergic waves guide the refinement of retinal targets and that glutamatergic waves may also support the wiring of retinal receptive fields.

High density microelectrode arrays (MEAs) provide extracellular recordings from thousand of electrodes (http://www.3brain.com) and offer novel capabilities to investigate electrophysiological signaling in cultured neuronal networks and in ex vivo brain tissues. In this study we report on our recent technological and data analysis advancements to investigate the propagation and the interplay of spontaneous and electrically evoked activities in cultured networks. To do so, a novel high density MEA with on-chip stimulating electrodes was realized. It provides whole-array recordings from 4096 electrodes (pitch of 81 um, active area of 8 mm by 8 mm) and electrical stimulation from 16 electrodes located every 8 recording sites. Here, this device was used to interface hippocampal neuronal networks. From the second week in vitro [1] these cultures display a peculiar intrinsic firing regime characterized by periodic synchronized network-wide bursts. These network bursts originate from specific ignition sites of the cultured network (i.e. characterized by more excitable cells) and can propagate through the entire network. These propagations are informative of the underlying network connectivity and their classification based on their spatiotemporal patterns might elucidate the network's organization and its ongoing dynamic. Previous studies [1] have described the trajectories of these propagations by tracking their center of activity trajectory (CAT). Although CATs provide a good overall description of spatiotemporal patterns, they are not suited for fine studies on these propagations. Here we have adopted a more rigorous approach by applying dimensional reduction techniques that take advantage of the redundancy and of the sparseness of multi-unit recordings. Our results show that by Principal Component Analysis (PCA) we can properly reconstruct the time course of spatiotemporal propagations with a minimal set of three components (i.e. explaining ~90% of the variance of the trajectories). The PCA classification based approach allowed us to characterize both intrinsic and electrically evoked network-wide propagations. Interestingly, our results show that electrical stimulation can evoke (i) distinctive propagations that depend on the specific spatial-temporal properties of the stimulus as well as (ii) propagations that are already expressed in the intrinsic 'repertoire' of network bursts. Here we will discuss these results and our observations on the interplay between intrinsic and electrically evoked network-wide propagations.

Multielectrode arrays (MEAs) for electrophysiological studies in neuroscience can nowadays be realized with Complementary Metal Oxide Semiconductor (CMOS) technology to provide large active areas with several thousands of simultaneously recording microelectrodes and electrode densities with inter-electrode separations approaching cellular sizes. This provides spatial and temporal resolutions to literally image electrophysiological activity propagations. Here, we focus on the achieved sensing capabilities of a 64×64 electrode array as well as on the actuation performances of a novel CMOS-chip integrating 16 electrodes for electrical stimulation. We report and discuss our recent results obtained from neuronal cell cultures, brain slices and mouse retina preparations.

It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.

The acquisition, analysis and representation of experimental data describing both anatomical and functional information at cellular level is an innovative opportunity to investigate neuronal network processing and organization. In this paper we propose an image processing pipeline to study in-vitro neuronal networks with a joint analysis of anatomy and electrophysiology. Neuronal nuclei are detected by segmenting fluorescence images of neuronal cultures. The high resolution Multi Electrode Arrays (MEAs) technology is used to collect functional information on cellular electrophysiological activity. Finally, detailed maps, representing both structural and functional information, are obtained which provide statistics on neuron distribution and spiking activity.

Based on experiments performed with high-resolution Active Pixel Sensor microelectrode arrays (APS-MEAs) coupled with spontaneously active hippocampal cultures, this work investigates the spatial resolution effects of the neuroelectronic interface on the analysis of the recorded electrophysiological signals. The adopted methodology consists, first, in recording the spontaneous activity at the highest spatial resolution (interelectrode separation of 21 mum) from the whole array of 4096 microelectrodes. Then, the full resolution dataset is spatially downsampled in order to evaluate the effects on raster plot representation, array-wide spike rate (AWSR), mean firing rate (MFR) and mean bursting rate (MBR). Furthermore, the effects of the array-to-network relative position are evaluated by shifting a subset of equally spaced electrodes on the entire recorded area. Results highlight that MFR and MBR are particularly influenced by the spatial resolution provided by the neuroelectronic interface. On high-resolution large MEAs, such analysis better represent the time-based parameterization of the network dynamics. Finally, this work suggest interesting capabilities of high-resolution MEAs for spatial-based analysis in dense and low-dense neuronal preparation for investigating signaling at both local and global neuronal circuitries.

During differentiation, cell processes initiate exploratory motion to create connections with other cells thereby creating a tissue architecture that is capable of performing complex tasks. The interplay between mechanical and chemical stimuli seems necessary for triggering the proper biochemical reactions that eventually lead to the functional organization of cells and tissue[1].There are different approaches for studying the focused mechanical-chemical transduction, either at single cell[2] level or at tissue[3] level. To better understand tissue development ( cell differentiation, cells contact formation, tissue organization), we would like to bridge the gap between experiments on single cells and complex tissues. Therefore we are developing a system for combining optical techniques such as optical tweezers[4] and a laser dissector with electrophysiological tools. Optical tweezers permit to apply localized mechano-chemical stimuli onto cells[5] while a laser dissector can alter individual neuronal connections[6]. By adopting neuronal networks as a biological model, neural signal transmission affected by such external stimuli can be recorded non-invasively by microelectrode arrays.Ongoing work is targeted at correlating the temporary or lasting changes in neural networks to the type and site of the stimulus.1. Pampaloni, F. et al. Nat. Rev. Mol. Cell Biol. 8, (2007).2. Peyton, S.R. et al. Cell Biochemistry and Biophysics 47, (2007).3. Judex, S. et al. J. Biomech. 30, (1997).4. Neuman, K.C. & Block,S.M. Review of Scientific Instruments 75, (2004).5. Cojoc, D. et al. PLoS. ONE. 2, (2007).6. Colombelli J. et al. Methods Cell Biol. 82, 267(2007).

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.

A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 microm on an active area of 2.67 mm x 2.67 mm, and in the zooming mode, temporal resolutions down to 8 micros on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 microV (rms)) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.

High-density microelectrode arrays (MEAs) enabled by recent developments of microelectronic circuits (CMOS-MEA) and providing spatial resolutions down to the cellular level open the perspective to access simultaneously local and overall neuronal network activities expressed by in vitro preparations. The short inter-electrode separation results in a gain of information on the micro-circuit neuronal dynamics and signal propagation, but requires the careful evaluation of the time resolution as well as the assessment of possible cross-talk artifacts. In this respect, we have realized and tested Pt high-density (HD)-MEAs featuring four local areas with 10microm inter-electrode spacing and providing a suitable noise level for the assessment of the high-density approach. First, simulated results show how possible artifacts (duplicated spikes) can be theoretically observed on nearby microelectrodes only for very high-shunt resistance values (e.g. R(sh)=50 kOmega generates up to 60% of false positives). This limiting condition is not compatible with typical experimental conditions (i.e. dense but not confluent cultures). Experiments performed on spontaneously active cortical neuronal networks show that spike synchronicity decreases by increasing the time resolution and analysis results show that the detected synchronous spikes on nearby electrodes are likely to be unresolved (in time) fast local propagations. Finally, functional connectivity analysis results show stronger local connections than long connections spread homogeneously over the whole network demonstrating the expected gain in detail provided by the spatial resolution.

Regeneration of functional connectivity within a neural network after different degrees of lesion is of utmost clinical importance. To test pharmacological approaches aimed at recovering from a total or partial damage of neuronal connections within a circuit, it is necessary to develop a precise method for controlled ablation of neuronal processes. We combined a UV laser microdissector to ablate neural processes in vitro at single neuron and neural network level with infrared holographic optical tweezers to carry out force spectroscopy measurements. Simultaneous force spectroscopy, down to the sub-pico-Newton range, was performed during laser dissection to quantify the tension release in a partially ablated neurite. Therefore, we could control and measure the damage inflicted to an individual neuronal process. To characterize the effect of the inflicted injury on network level, changes in activity of neural subpopulations were monitored with subcellular resolution and overall network activity with high temporal resolution by concurrent calcium imaging and microelectrode array recording. Neuronal connections have been sequentially ablated and the correlated changes in network activity traced and mapped. With this unique combination of electrophysiological and optical tools, neural activity can be studied and quantified in response to controlled injury at the subcellular, cellular, and network level.

In this work, we investigate the spontaneous bursting behaviour expressed by in vitro hippocampal networks by using a high-resolution CMOS-based microelectrode array (MEA), featuring 4096 electrodes, inter-electrode spacing of 21 µm and temporal resolution of 130 µs. In particular, we report an original development of an adapted analysis method enabling us to investigate spatial and temporal patterns of activity and the interplay between successive network bursts (NBs). We first defined and detected NBs, and then, we analysed the spatial and temporal behaviour of these events with an algorithm based on the centre of activity trajectory. We further refined the analysis by using a technique derived from statistical mechanics, capable of distinguishing the two main phases of NBs, i.e. (i) a propagating and (ii) a reverberating phase, and by classifying the trajectory patterns. Finally, this methodology was applied to signal representations based on spike detection, i.e. the instantaneous firing rate, and directly based on voltage-coded raw data, i.e. activity movies. Results highlight the potentialities of this approach to investigate fundamental issues on spontaneous neuronal dynamics and suggest the hypothesis that neurons operate in a sort of 'team' to the perpetuation of the transmission of the same information.

This paper presents a chip-based electrophysiological platform enabling the study of micro- and macro-circuitry in in-vitro neuronal preparations. The approach is based on a 64x64 microelectrode array device providing extracellular electrophysiological activity recordings with high spatial (21 microm of electrode separation) and temporal resolution (from 0.13 ms for 4096 microelectrodes down to 8 micros for 64 microelectrodes). Applied to in-vitro neuronal preparations, we show how this approach enables neuronal signals to be acquired for investigating neuronal activity from single cells and microcircuits to large scale neuronal networks. The main elements of the platform are the metallic microelectrode array (MEA) implemented in Complementary Metal Oxide Semiconductor (CMOS) technology similar to a light imager, the in-pixel integrated low-noise amplifiers (11 microVrms) and the high-speed random addressing logic. The chip is combined with a real-time acquisition system providing the capability to record at 7.8 kHz/electrode the whole array and to process the acquired signals.

3D plasmonic nanoantennas were fabricated on active biodevices for in-vitro neuroscience experiments. The technique consents to realize nanoantennas patterns suitable for neurons culture and that can be used concurrently as intracellular nanoelectrodes and spectroscopic probes.

A platform based on an active-pixel-sensor electrode array (APS-MEA) for high-resolution imaging of in-vitro electrogenic cell cultures is presented, characterized and validated under culture conditions. The system enables full frame acquisition at 8 kHz from 4096 microelectrodes integrated with separations of 21 microm and zoomed area acquisition with temporal resolutions down to 8 micros. This bi-modal acquisition feature opens new perspectives in particular for neuronal activity analysis and for the correlation of micro-scale and macro-scale behaviors. The low-noise performances of the integrated amplifier (11 microVRMS) combined with a hardware implementation reflecting image-/video-concepts enable high-resolution acquisitions with real-time processing capabilities adapted to the handling of the large amount of acquired data.