Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

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.

Sloppiness in spontaneously active neuronal networks.

Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function.

Specific Neuron Placement on Gold and Silicon Nitride-Patterned Substrates through a Two-Step Functionalization Method.

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.

Following the ontogeny of retinal waves: pan-retinal recordings of population dynamics in the neonatal mouse.

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.

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.

Modeling a population of retinal ganglion cells with restricted Boltzmann machines.

The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the visual cortex. Due to the importance of its role, modeling the retina to advance in interpreting its spiking activity output is a well studied problem. In particular, it has been shown that latent variable models can be used to model the joint distribution of Retinal Ganglion Cells (RGCs). In this work, we validate the applicability of Restricted Boltzmann Machines to model the spiking activity responses of a large a population of RGCs recorded with high-resolution electrode arrays. In particular, we show that latent variables can encode modes in the RGC activity distribution that are closely related to the visual stimuli. In contrast to previous work, we further validate our findings by comparing results associated with recordings from retinas under normal and altered encoding conditions obtained by pharmacological manipulation. In these conditions, we observe that the model reflects well-known physiological behaviors of the retina. Finally, we show that we can also discover temporal patterns, associated with distinct dynamics of the stimuli.

Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks.

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.

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.

Investigating the Effects of Mechanical Stimulation on Retinal Ganglion Cell Spontaneous Spiking Activity.

Mechanical forces are increasingly recognized as major regulators of several physiological processes at both the molecular and cellular level; therefore, a deep understanding of the sensing of these forces and their conversion into electrical signals are essential for studying the mechanosensitive properties of soft biological tissues. To contribute to this field, we present a dual-purpose device able to mechanically stimulate retinal tissue and to record the spiking activity of retinal ganglion cells (RGCs). This new instrument relies on combining ferrule-top micro-indentation, which provides local measurements of viscoelasticity, with high-density multi-electrode array (HD-MEAs) to simultaneously record the spontaneous activity of the retina. In this paper, we introduce this instrument, describe its technical characteristics, and present a proof-of-concept experiment that shows how RGC spiking activity of explanted mice retinas respond to mechanical micro-stimulations of their photoreceptor layer. The data suggest that, under specific conditions of indentation, the retina perceive the mechanical stimulation as modulation of the visual input, besides the longer time-scale of activation, and the increase in spiking activity is not only localized under the indentation probe, but it propagates across the retinal tissue.

Fabrication of Multielectrode Arrays for Neurobiology Applications.

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.

A Synchronous Neural Recording Platform for Multiple High-Resolution CMOS Probes and Passive Electrode Arrays.

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.

State-dependent representation of stimulus-evoked activity in high-density recordings of neural cultures.

Neuronal responses to external stimuli vary from trial to trial partly because they depend on continuous spontaneous variations of the state of neural circuits, reflected in variations of ongoing activity prior to stimulus presentation. Understanding how post-stimulus responses relate to the pre-stimulus spontaneous activity is thus important to understand how state dependence affects information processing and neural coding, and how state variations can be discounted to better decode single-trial neural responses. Here we exploited high-resolution CMOS electrode arrays to record simultaneously from thousands of electrodes in in-vitro cultures stimulated at specific sites. We used information-theoretic analyses to study how ongoing activity affects the information that neuronal responses carry about the location of the stimuli. We found that responses exhibited state dependence on the time between the last spontaneous burst and the stimulus presentation and that the dependence could be described with a linear model. Importantly, we found that a small number of selected neurons carry most of the stimulus information and contribute to the state-dependent information gain. This suggests that a major value of large-scale recording is that it individuates the small subset of neurons that carry most information and that benefit the most from knowledge of its state dependence.

High-resolution bioelectrical imaging of Aß-induced network dysfunction on CMOS-MEAs for neurotoxicity and rescue studies.

Neurotoxicity and the accumulation of extracellular amyloid-beta1-42 (Aß) 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 Aß-neurotoxicity. We also demonstrate the potential of this in vitro model to test a plausible hypothesis underlying the Aß-neurotoxicity and to assay potential therapeutic approaches. Specifically, by quantifying N-methyl D-aspartate (NMDA) concentration-dependent effects in comparison with low-concentration allogenic-Aß, we confirm the role of extrasynaptic-NMDA receptors activation that may contribute to Aß-neurotoxicity. Finally, we assess the potential rescue of neural stem cells (NSCs) and of two pharmacotherapies, memantine and saffron, for reversing Aß-neurotoxicity and rescuing network-wide firing.

Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays.

We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.

Selective Targeting of Neurons with Inorganic Nanoparticles: Revealing the Crucial Role of Nanoparticle Surface Charge.

Nanoparticles (NPs) are increasingly used in biomedical applications, but the factors that influence their interactions with living cells need to be elucidated. Here, we reveal the role of NP surface charge in determining their neuronal interactions and electrical responses. We discovered that 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.

Electrical Responses and Spontaneous Activity of Human iPS-Derived Neuronal Networks Characterized for 3-month Culture with 4096-Electrode Arrays.

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 3 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 3-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 3 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.

Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings.

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.

Investigating cell culture dynamics combining high density recordings with dimensional reduction techniques.

High density multielectrode array recordings with CMOS-MEAs allow to monitor cell culture activity with unprecedent details respect to previous recording techniques. This is clarifying how network activity develops and is motivating the development of novel data analysis tools. Here, in order to advance in the exploitation of the richness of these large-scale array recordings, we introduce a principal component analysis approach that aims at improving on existing methodologies to describe neural activity events within large networks.

Bridging the gap in connectomic studies: A particle filtering framework for estimating structural connectivity at network scale.

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.

High-density MEA recordings unveil the dynamics of bursting events in Cell Cultures.

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.