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

Graphene-based microfluidic perforated microelectrode arrays for retinal electrophysiological studies

Perforated microelectrode arrays (pMEAs) have become essential tools for ex vivo retinal electrophysiological studies. pMEAs increase the nutrient supply to the explant and alleviate the accentuated curvature of the retina, allowing for long-term culture and intimate contacts between the retina and electrodes for electrophysiological measurements. However, commercial pMEAs are not compatible with in situ high-resolution optical imaging and lack the capability of controlling the local microenvironment, which are highly desirable features for relating function to anatomy and probing physiological and pathological mechanisms in retina. Here we report on microfluidic pMEAs (μpMEAs) that combine transparent graphene electrodes and the capability of locally delivering chemical stimulation. We demonstrate the potential of μpMEAs by measuring the electrical response of ganglion cells to locally delivered high K+ stimulation under controlled microenvironments. Importantly, the capability for high-resolution confocal imaging of the retina tissue on top of the graphene electrodes allows for further analyses of the electrical signal source. The new capabilities provided by μpMEAs could allow for retinal electrophysiology assays to address key questions in retinal circuitry studies.

Longitudinal in vivo Ca2+ imaging reveals dynamic activity changes of diseased retinal ganglion cells at the single-cell level

Retinal ganglion cells (RGCs) are heterogeneous projection neurons that convey distinct visual features from the retina to brain. Here, we present a high-throughput in vivo RGC activity assay in response to light stimulation using noninvasive Ca2+ imaging of thousands of RGCs simultaneously in living mice. Population and single-cell analyses of longitudinal RGC Ca2+ imaging reveal distinct functional responses of RGCs and unprecedented individual RGC activity conversions during traumatic and glaucomatous degeneration. This study establishes a foundation for future in vivo RGC function classifications and longitudinal activity evaluations using more advanced imaging techniques and visual stimuli under normal, disease, and neural repair conditions. These analyses can be performed at both the population and single-cell levels using temporal and spatial information, which will be invaluable for understanding RGC pathophysiology and identifying functional biomarkers for diverse optic neuropathies.

Preferential modulation of individual retinal ganglion cells by electrical stimulation

OBJECTIVE

Retinal prostheses have been able to recover partial vision in blind patients with retinal degeneration by electrically stimulating surviving cells in the retina, such as retinal ganglion cells (RGCs), but the restored vision is limited. This is partly due to non-preferential stimulation of all RGCs near a single stimulating electrode, which include cells that conflict in their response properties and their contribution to the vision process. Our study proposes a stimulation strategy to preferentially stimulate individual RGCs based on their temporal electrical receptive fields (tERFs).

APPROACH

We recorded the responses of RGCs using whole-cell current-clamp and demonstrated the stimulation strategy, first using intracellular stimulation, then via extracellular stimulation.

MAIN RESULTS

We successfully reconstructed the tERFs according to the RGC response to Gaussian white noise current stimulation. The characteristics of the tERFs were extracted and compared according to the morphological and light response types of the cells. By re-delivering stimulation trains that are composed of the tERFs obtained from different cells, we could target individual RGCs as the cells showed lower activation thresholds to their own tERFs.

SIGNIFICANCE

This proposed stimulation strategy implemented in the next generation of recording and stimulating retinal prostheses may improve the quality of artificial vision.

Visual properties of human retinal ganglion cells

The retinal output is the sole source of visual information for the brain. Studies in non-primate mammals estimate that this information is carried by several dozens of retinal ganglion cell types, each informing the brain about different aspects of a visual scene. Even though morphological studies of primate retina suggest a similar diversity of ganglion cell types, research has focused on the function of only a few cell types. In human retina, recordings from individual cells are anecdotal or focus on a small subset of identified types. Here, we present the first systematic ex-vivo recording of light responses from 342 ganglion cells in human retinas obtained from donors. We find a great variety in the human retinal output in terms of preferences for positive or negative contrast, spatio-temporal frequency encoding, contrast sensitivity, and speed tuning. Some human ganglion cells showed similar response behavior as known cell types in other primate retinas, while we also recorded light responses that have not been described previously. This first extensive description of the human retinal output should facilitate interpretation of primate data and comparison to other mammalian species, and it lays the basis for the use of ex-vivo human retina for in-vitro analysis of novel treatment approaches.

Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review

Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.

Neuronal circuits and the magnetic sense: central questions

Magnetoreception is the ability to sense the Earth's magnetic field, which is used for orientation and navigation. Behavioural experiments have shown that it is employed by many species across all vertebrate classes; however, our understanding of how magnetic information is processed and integrated within the central nervous system is limited. In this Commentary, we review the progress in birds and rodents, highlighting the role of the vestibular and trigeminal systems as well as that of the hippocampus. We reflect on the strengths and weaknesses of the methodologies currently at our disposal, the utility of emerging technologies and identify questions that we feel are critical for the advancement of the field. We expect that magnetic circuits are likely to share anatomical motifs with other senses, which culminates in the formation of spatial maps in telencephalic areas of the brain. Specifically, we predict the existence of spatial cells that encode defined components of the Earth's magnetic field.

Massively parallel microwire arrays integrated with CMOS chips for neural recording

Multi-channel electrical recordings of neural activity in the brain is an increasingly powerful method revealing new aspects of neural communication, computation, and prosthetics. However, while planar silicon-based CMOS devices in conventional electronics scale rapidly, neural interface devices have not kept pace. Here, we present a new strategy to interface silicon-based chips with three-dimensional microwire arrays, providing the link between rapidly-developing electronics and high density neural interfaces. The system consists of a bundle of microwires mated to large-scale microelectrode arrays, such as camera chips. This system has excellent recording performance, demonstrated via single unit and local-field potential recordings in isolated retina and in the motor cortex or striatum of awake moving mice. The modular design enables a variety of microwire types and sizes to be integrated with different types of pixel arrays, connecting the rapid progress of commercial multiplexing, digitisation and data acquisition hardware together with a three-dimensional neural interface.

Transplantation of Retinal Ganglion Cells Derived from Male Germline Stem Cell as a Potential Treatment to Glaucoma

Glaucoma is characterized by retinal ganglion cell (RGC) degeneration and is the second leading cause of blindness worldwide. However, current treatments such as eye drop or surgery have limitations and do not target the loss of RGC. Regenerative therapy using embryonic stem cells (ESCs) holds a promising option, but ethical concern hinders clinical applications on human subjects. In this study, we employed spermatogonial stem cells (SSCs) as an alternative source of ESCs for cell-based regenerative therapy in mouse glaucoma model. We generated functional RGCs from SSCs with a two-step protocol without applying viral transfection or chemical induction. SSCs were first dedifferentiated to embryonic stem-like cells (SSC-ESCs) that resemble ESCs in morphology, gene expression signatures, and stem cell properties. The SSC-ESCs then differentiated toward retinal lineages. We showed SSC-ESC-derived retinal cells expressed RGC-specific marker Brn3b and functioned as bona fide RGCs. To allow in vivo RGC tracing, Brn3b-EGFP reporter SSC-ESCs were generated and the derived RGCs were subsequently transplanted into the retina of glaucoma mouse models by intravitreal injection. We demonstrated that the transplanted RGCs could survive in host retina for at least 10 days after transplantation. SSC-ESC-derived RGCs can thus potentially be a novel alternative to replace the damaged RGCs in glaucomatous retina.

Large-Scale, High-Resolution Microelectrode Arrays for Interrogation of Neurons and Networks

High-density microelectrode arrays (HD-MEAs) are increasingly being used for the observation and manipulation of neurons and networks in vitro. Large-scale electrode arrays allow for long-term extracellular recording of the electrical activity from thousands of neurons simultaneously. Beyond population activity, it has also become possible to extract information of single neurons at subcellular level (e.g., the propagation of action potentials along axons). In effect, HD-MEAs have become an electrical imaging platform for label-free extraction of the structure and activation of cells in cultures and tissues. The quality of HD-MEA data depends on the resolution of the electrode array and the signal-to-noise ratio. In this chapter, we begin with an introduction to HD-MEA signals. We provide an overview of the developments on complementary metal-oxide-semiconductor or CMOS-based HD-MEA technology. We also discuss the factors affecting the performance of HD-MEAs and the trending application requirements that drive the efforts for future devices. We conclude with an outlook on the potential of HD-MEAs for advancing basic neuroscience and drug discovery.

Technologies to Study Action Potential Propagation With a Focus on HD-MEAs

Axons convey information in neuronal circuits via reliable conduction of action potentials (APs) from the axon initial segment (AIS) to the presynaptic terminals. Recent experimental findings increasingly evidence that the axonal function is not limited to the simple transmission of APs. Advances in subcellular-resolution recording techniques have shown that axons display activity-dependent modulation in spike shape and conduction velocity, which influence synaptic strength and latency. We briefly review here, how recent methodological developments facilitate the understanding of the axon physiology. We included the three most common methods, i.e., genetically encoded voltage imaging (GEVI), subcellular patch-clamp and high-density microelectrode arrays (HD-MEAs). We then describe the potential of using HD-MEAs in studying axonal physiology in more detail. Due to their robustness, amenability to high-throughput and high spatiotemporal resolution, HD-MEAs can provide a direct functional electrical readout of single cells and cellular ensembles at subcellular resolution. HD-MEAs can, therefore, be employed in investigating axonal pathologies, the effects of large-scale genomic interventions (e.g., with RNAi or CRISPR) or in compound screenings. A combination of extracellular microelectrode arrays (MEAs), intracellular microelectrodes and optical imaging may potentially reveal yet unexplored repertoires of axonal functions.

Automatic spike sorting for high-density microelectrode arrays

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the "curse of dimensionality" and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

Identification of Retinal Ganglion Cell Firing Patterns Using Clustering Analysis Supplied with Failure Diagnosis

An important goal in visual neuroscience is to understand how neuronal population coding in vertebrate retina mediates the broad range of visual functions. Microelectrode arrays interface on isolated retina registers a collective measure of the spiking dynamics of retinal ganglion cells (RGCs) by probing them simultaneously and in large numbers. The recorded data stream is then processed to identify spike trains of individual RGCs by efficient and scalable spike detection and sorting routines. Most spike sorting software packages, available either commercially or as freeware, combine automated steps with judgment calls by the investigator to verify the quality of sorted spikes. This work focused on sorting spikes of RGCs into clusters using an integrated analytical platform for the data recorded during visual stimulation of wild-type mice retinas with whole field stimuli. After spike train detection, we projected each spike onto two feature spaces: a parametric space and a principal components space. We then applied clustering algorithms to sort spikes into separate clusters. To eliminate the need for human intervention, the initial clustering results were submitted to diagnostic tests that evaluated the results to detect the sources of failure in cluster assignment. This failure diagnosis formed a decision logic for diagnosable electrodes to enhance the clustering quality iteratively through rerunning the clustering algorithms. The new clustering results showed that the spike sorting accuracy was improved. Subsequently, the number of active RGCs during each whole field stimulation was found, and the light responsiveness of each RGC was identified. Our approach led to error-resilient spike sorting in both feature extraction methods; however, using parametric features led to less erroneous spike sorting compared to principal components, particularly for low signal-to-noise ratios. As our approach is reliable for retinal signal processing in response to simple visual stimuli, it could be applied to the evaluation of disrupted physiological signaling in retinal neurodegenerative diseases.

Non-rectangular waveforms are more charge-efficient than rectangular one in eliciting network-mediated responses of ON type retinal ganglion cells

OBJECTIVE

For individuals blinded by outer retinal degenerative diseases, retinal prostheses would be a promising option to restore sight. Unfortunately, however, the best performance of existing devices is still far removed from normal vision. One possible reason for the shortcoming is thought to be suboptimal stimulation conditions such as the waveform shape of electric stimulus. In this study, we explored the effects of varying waveforms on network-mediated responses arising in retinal ganglion cells (RGCs).

APPROACH

We used a cell-attached patch clamp technique to record RGC spiking activities in the isolated mouse retina. ON alpha RGCs were targeted by soma size and their light responses to stationary spot flashes. Spiking in targeted RGCs was measured in response to an epiretinally-delivered cathodal current pulse in four waveforms: rectangular, center triangular, increasing and decreasing ramp shapes. Each waveform was tested at three durations (20, 10, and 5 ms) with adjusted amplitude for a range of total charges (50-400 nC).

MAIN RESULTS

ON alpha RGCs always generated two bursts of spikes in responses to all stimuli conditions we tested. However, at a given charge, effects of differing waveforms were distinct in the two bursts. For the first burst, the increasing ramp was most effective among the four waveforms (p  <  0.05 for all pairwise comparisons with other waveforms). For example, in responses arising from 20 ms-long stimuli, the increasing ramp evoked ~44% more spikes on average than the rectangular shape which is the typical choice of neural stimulation. Also, the rectangular stimulus evoked the weakest response in the delayed burst arising from pulses of every duration. For instance, 20 ms-long stimuli in the three non-rectangular waveforms showed ~23% or more increment in spike counts compared to response arising from the rectangular one; but there was no statistical difference in response magnitudes across the non-rectangular waveforms.

SIGNIFICANCE

Although the rectangular waveform has been primarily used in retinal prostheses our results indicate that rectangular stimulus is not optimal for network-mediated responses of ON alpha RGCs. Instead, non-rectangular waveforms evoke stronger responses at a given charge, indicating higher charge-efficiency. Therefore, non-rectangular waveforms are expected to enhance clinical efficacy of retinal prostheses.

How Diverse Retinal Functions Arise from Feedback at the First Visual Synapse

Many brain regions contain local interneurons of distinct types. How does an interneuron type contribute to the input-output transformations of a given brain region? We addressed this question in the mouse retina by chemogenetically perturbing horizontal cells, an interneuron type providing feedback at the first visual synapse, while monitoring the light-driven spiking activity in thousands of ganglion cells, the retinal output neurons. We uncovered six reversible perturbation-induced effects in the response dynamics and response range of ganglion cells. The effects were enhancing or suppressive, occurred in different response epochs, and depended on the ganglion cell type. A computational model of the retinal circuitry reproduced all perturbation-induced effects and led us to assign specific functions to horizontal cells with respect to different ganglion cell types. Our combined experimental and theoretical work reveals how a single interneuron type can differentially shape the dynamical properties of distinct output channels of a brain region.

A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain 'ground truth' data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.

A very large-scale microelectrode array for cellular-resolution electrophysiology

In traditional electrophysiology, spatially inefficient electronics and the need for tissue-to-electrode proximity defy non-invasive interfaces at scales of more than a thousand low noise, simultaneously recording channels. Using compressed sensing concepts and silicon complementary metal-oxide-semiconductors (CMOS), we demonstrate a platform with 65,536 simultaneously recording and stimulating electrodes in which the per-electrode electronics consume an area of 25.5 μm by 25.5 μm. Application of this platform to mouse retinal studies is achieved with a high-performance processing pipeline with a 1 GB/s data rate. The platform records from 65,536 electrodes concurrently with a ~10 µV r.m.s. noise; senses spikes from more than 34,000 electrodes when recording across the entire retina; automatically sorts and classifies greater than 1700 neurons following visual stimulation; and stimulates individual neurons using any number of the 65,536 electrodes while observing spikes over the entire retina. The approaches developed here are applicable to other electrophysiological systems and electrode configurations.

Large electronics limit low-noise, non-invasive electrophysiological measurements to a thousand simultaneously recording channels. Here the authors build an array of 65k simultaneously recording and stimulating electrodes and use it to sort and classify single neurons across the entire mouse retina.

Large scale matching of function to the genetic identity of retinal ganglion cells

Understanding the role of neurons in encoding and transmitting information is a major goal in neuroscience. This requires insight on the data-rich neuronal spiking patterns combined, ideally, with morphology and genetic identity. Electrophysiologists have long experienced the trade-offs between anatomically-accurate single-cell recording techniques and high-density multi-cellular recording methods with poor anatomical correlations. In this study, we present a novel technique that combines large-scale micro-electrode array recordings with genetic identification and the anatomical location of the retinal ganglion cell soma. This was obtained through optogenetic stimulation and subsequent confocal imaging of genetically targeted retinal ganglion cell sub-populations in the mouse. With the many molecular options available for optogenetic gene expression, we view this method as a versatile tool for matching function to genetic classifications, which can be extended to include morphological information if the density of labelled cells is at the correct level.

Causal evidence for retina-dependent and -independent visual motion computations in mouse cortex

How neuronal computations in the sensory periphery contribute to computations in the cortex is not well understood. We examined this question in the context of visual-motion processing in the retina and primary visual cortex (V1) of mice. We disrupted retinal direction selectivity, either exclusively along the horizontal axis using FRMD7 mutants or along all directions by ablating starburst amacrine cells, and monitored neuronal activity in layer 2/3 of V1 during stimulation with visual motion. In control mice, we found an over-representation of cortical cells preferring posterior visual motion, the dominant motion direction an animal experiences when it moves forward. In mice with disrupted retinal direction selectivity, the over-representation of posterior-motion-preferring cortical cells disappeared, and their responses at higher stimulus speeds were reduced. This work reveals the existence of two functionally distinct, sensory-periphery-dependent and -independent computations of visual motion in the cortex.

Combination of High-density Microelectrode Array and Patch Clamp Recordings to Enable Studies of Multisynaptic Integration

We present a novel, all-electric approach to record and to precisely control the activity of tens of individual presynaptic neurons. The method allows for parallel mapping of the efficacy of multiple synapses and of the resulting dynamics of postsynaptic neurons in a cortical culture. For the measurements, we combine an extracellular high-density microelectrode array, featuring 11’000 electrodes for extracellular recording and stimulation, with intracellular patch-clamp recording. We are able to identify the contributions of individual presynaptic neurons - including inhibitory and excitatory synaptic inputs - to postsynaptic potentials, which enables us to study dendritic integration. Since the electrical stimuli can be controlled at microsecond resolution, our method enables to evoke action potentials at tens of presynaptic cells in precisely orchestrated sequences of high reliability and minimum jitter. We demonstrate the potential of this method by evoking short- and long-term synaptic plasticity through manipulation of multiple synaptic inputs to a specific neuron.

Direct Interfacing of Neurons to Highly Integrated Microsystems

The use of large high-density transducer arrays enables fundamentally new neuroscientific insights through enabling high-throughput monitoring of action potentials of larger neuronal networks (> 1000 neurons) over extended time to see effects of disturbances or developmental effects, and through facilitating detailed investigations of neuronal signaling characteristics at subcellular level, for example, the study of axonal signal propagation that has been largely inaccessible to established methods. Applications include research in neural diseases and pharmacology.

Recent progress in multi-electrode spike sorting methods

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

Extracellularly Recorded Somatic and Neuritic Signal Shapes and Classification Algorithms for High-Density Microelectrode Array Electrophysiology

High-density microelectrode arrays (HDMEA) have been recently introduced to study principles of neural function at high spatial resolution. However, the exact nature of the experimentally observed extracellular action potentials (EAPs) is still incompletely understood. The soma, axon and dendrites of a neuron can all exhibit regenerative action potentials that could be sensed with HDMEA electrodes. Here, we investigate the contribution of distinct neuronal sources of activity in HDMEA recordings from low-density neuronal cultures. We recorded EAPs with HDMEAs having 11,011 electrodes and then fixed and immunostained the cultures with β3-tubulin for high-resolution fluorescence imaging. Immunofluorescence images overlaid with the activity maps showed EAPs both at neuronal somata and distal neurites. Neuritic EAPs had mostly narrow triphasic shapes, consisting of a positive, a pronounced negative peak and a second positive peak. EAPs near somata had wide monophasic or biphasic shapes with a main negative peak, and following optional positive peak. We show that about 86% of EAP recordings consist of somatic spikes, while the remaining 14% represent neuritic spikes. Furthermore, the adaptation of the waveform shape during bursts of these neuritic spikes suggested that they originate from axons, rather than from dendrites. Our study improves the understanding of HDMEA signals and can aid in the identification of the source of EAPs.

Electrical Identification and Selective Microstimulation of Neuronal Compartments Based on Features of Extracellular Action Potentials

A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation. We developed strategies to electrically identify any neuron in the network, while subcellular spatial resolution recording of extracellular action potential (AP) traces enabled their assignment to the axon initial segment (AIS), axonal arbor and proximal somatodendritic compartments. Stimulation at the AIS required low voltages and provided immediate, selective and reliable neuronal activation, whereas stimulation at the soma required high voltages and produced delayed and unreliable responses. Subthreshold stimulation at the soma depolarized the somatic membrane potential without eliciting APs.

Structures of Neural Correlation and How They Favor Coding

The neural representation of information suffers from "noise"-the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina.

A method for electrophysiological characterization of hamster retinal ganglion cells using a high-density CMOS microelectrode array

Knowledge of neuronal cell types in the mammalian retina is important for the understanding of human retinal disease and the advancement of sight-restoring technology, such as retinal prosthetic devices. A somewhat less utilized animal model for retinal research is the hamster, which has a visual system that is characterized by an area centralis and a wide visual field with a broad binocular component. The hamster retina is optimally suited for recording on the microelectrode array (MEA), because it intrinsically lies flat on the MEA surface and yields robust, large-amplitude signals. However, information in the literature about hamster retinal ganglion cell functional types is scarce. The goal of our work is to develop a method featuring a high-density (HD) complementary metal-oxide-semiconductor (CMOS) MEA technology along with a sequence of standardized visual stimuli in order to categorize ganglion cells in isolated Syrian Hamster (Mesocricetus auratus) retina. Since the HD-MEA is capable of recording at a higher spatial resolution than most MEA systems (17.5 μm electrode pitch), we were able to record from a large proportion of RGCs within a selected region. Secondly, we chose our stimuli so that they could be run during the experiment without intervention or computation steps. The visual stimulus set was designed to activate the receptive fields of most ganglion cells in parallel and to incorporate various visual features to which different cell types respond uniquely. Based on the ganglion cell responses, basic cell properties were determined: direction selectivity, speed tuning, width tuning, transience, and latency. These properties were clustered to identify ganglion cell types in the hamster retina. Ultimately, we recorded up to a cell density of 2780 cells/mm² at 2 mm (42°) from the optic nerve head. Using five parameters extracted from the responses to visual stimuli, we obtained seven ganglion cell types.

Visual coding with a population of direction-selective neurons

The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions.

Microelectronics, bioinformatics and neurocomputation for massive neuronal recordings in brain circuits with large scale multielectrode array probes

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.

REGISTRO DE ACTIVIDAD ELÉCTRICA EN LA RETINA DE UNA RATA ALBINA EMPLEANDO UNA MATRIZ DE MICROELECTRODOS

<p>Las matrices de microelectrodos son dispositivos que permiten la detección de potenciales de acción o espigas en poblaciones de células excitables, ofreciendo varias aplicaciones en el campo de las neurociencias y la biología. Este trabajo muestra un protocolo para el registro de espigas en una población de células ganglionares retinales empleando una matriz de microelectrodos. La retina de una rata albina fue extraída y preparada para ser estimulada <em>in vitro </em>con luz led blanca, con el fin de registrar sus espigas evocadas ante estos estímulos. Cada microelectrodo puede registrar espigas de más de una célula ganglionar, razón por la cual se determinó a qué célula pertenece cada espiga aplicando un procedimiento conocido como “clasificación de espigas”. El trabajo permitió obtener el registro de un periodo de estimulación y otro de no estimulación, con el fin de representar los potenciales de acción evocados con luz y los espontáneos. Los registros fueron almacenados para visualizar las espigas de las células ganglionares y poder aplicar la herramienta de clasificación de espigas. De este modo, se almacenan los instantes de tiempo en los cuales cada célula ganglionar registrada generó potenciales de acción. Este trabajo conllevó al establecimiento de un protocolo de experimentación básico enfocado al uso de matrices MEA en el laboratorio de adquisición de potenciales extracelulares de la Universidad Antonio Nariño Sede Bogotá, no sólo para caracterizar los potenciales de acción de células ganglionares retinales, sino también para otro tipo de células que puedan ser estudiadas empleando matrices de microelectrodos.</p><p align="center"><strong>Recording of Electrical Activity in the Retina of an Albino Rat Employing a Microelectrode Array</strong></p><p>The microelectrode arrays (MEA) are devices that allow the detection of action potentials or spikes in populations of excitable cells, offering a wide spectrum of applications in topics of Neurosciences and Biology. This work describes a protocol for recording of spikes in a population of retinal ganglion cells employing a microelectrode array. The retina of an albino rat was dissected and prepared to be stimulated<em> in vitro </em>with white led light and to record their evoked spikes. Each microelectrode can record spikes from more than a ganglion cell, for which it was necessary to determine which cell fires each spike applying a procedure known as spike sorting. The work allowed to obtain the recording of a stimulation period and another of non-stimulation, representing evoked and spontaneous action potentials. The recordings were saved, in order to visualize the action potentials of the ganglion cells detected  and to apply a computational method for the spike sorting. In this way, it was saved the time stamps in which each action potential was fired by its respective cell. This work established a basic experimentation protocol focused to the use of MEA devices in the laboratory for acquisition of extracellular potentials at the Antonio Nariño University – Bogota Headquarters, not only for characterization of action potentials fired by retinal ganglion cells populations, but also for other kind of cells that can be studied employing MEA devices.</p><p> </p>

A network comprising short and long noncoding RNAs and RNA helicase controls mouse retina architecture

Brain regions, such as the cortex and retina, are composed of layers of uniform thickness. The molecular mechanism that controls this uniformity is not well understood. Here we show that during mouse postnatal development the timed expression of Rncr4, a retina-specific long noncoding RNA, regulates the similarly timed processing of pri-miR-183/96/182, which is repressed at an earlier developmental stage by RNA helicase Ddx3x. Shifting the timing of mature miR-183/96/182 accumulation or interfering with Ddx3x expression leads to the disorganization of retinal architecture, with the photoreceptor layer being most affected. We identify Crb1, a component of the adhesion belt between glial and photoreceptor cells, as a link between Rncr4-regulated miRNA metabolism and uniform retina layering. Our results suggest that the precise timing of glia–neuron interaction controlled by noncoding RNAs and Ddx3x is important for the even distribution of cells across layers.

The mammalian retina is a modular brain region, in which cell layers are of uniform thickness but the molecular mechanism controlling this process is not well understood. Here the authors identify a regulatory network consisting of the long noncoding RNA Rncr4, RNA helicase Ddx3x and miR-183/96/182 that controls the even distribution of cells across layers.

Recording large extracellular spikes in microchannels along many axonal sites from individual neurons

The numerous connections between neuronal cell bodies, made by their dendrites and axons, are vital for information processing in the brain. While dendrites and synapses have been extensively studied, axons have remained elusive to a large extent. We present a novel platform to study axonal physiology and information processing based on combining an 11,011-electrode high-density complementary metal-oxide semiconductor microelectrode array with a poly(dimethylsiloxane) channel device, which isolates axons from somas and, importantly, significantly amplifies recorded axonal signals. The combination of the microelectrode array with recording and stimulation capability with the microfluidic isolation channels permitted us to study axonal signal behavior at great detail. The device, featuring two culture chambers with over 30 channels spanning in between, enabled long-term recording of single spikes from isolated axons with signal amplitudes of 100 μV up to 2 mV. Propagating signals along axons could be recorded with 10 to 50 electrodes per channel. We (i) describe the performance and capabilities of our device for axonal electrophysiology, and (ii) present novel data on axonal signals facilitated by the device. Spontaneous action potentials with characteristic shapes propagated from somas along axons between the two compartments, and these unique shapes could be used to identify individual axons within channels that contained many axonal branches. Stimulation through the electrode array facilitated the identification of somas and their respective axons, enabling interfacing with different compartments of a single cell. Complex spike shapes observed in channels were traced back to single cells, and we show that more complicated spike shapes originate from a linear superposition of multiple axonal signals rather than signal distortion by the channels.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms

BACKGROUND

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times.

NEW METHOD

We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy.

RESULTS

ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval.

COMPARISON WITH EXISTING METHODS

ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers.

CONCLUSION

ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.

Revealing neuronal function through microelectrode array recordings

Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.

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.

Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites

Axons are traditionally considered stable transmission cables, but evidence of the regulation of action potential propagation demonstrates that axons may have more important roles. However, their small diameters render intracellular recordings challenging, and low-magnitude extracellular signals are difficult to detect and assign. Better experimental access to axonal function would help to advance this field. Here we report methods to electrically visualize action potential propagation and network topology in cortical neurons grown over custom arrays, which contain 11,011 microelectrodes and are fabricated using complementary metal oxide semiconductor technology. Any neuron lying on the array can be recorded at high spatio-temporal resolution, and simultaneously precisely stimulated with little artifact. We find substantial velocity differences occurring locally within single axons, suggesting that the temporal control of a neuron's output may contribute to neuronal information processing.

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 GABA_A 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.

Closed-Loop, Multichannel Experimentation Using the Open-Source NeuroRighter Electrophysiology Platform

Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, and neural dynamics. Although commercially available multichannel recording and stimulation systems are commonly used for studying neural processing at the network level, they provide little native support for real-time feedback. We developed the open-source NeuroRighter multichannel electrophysiology hardware and software platform for closed-loop multichannel control with a focus on accessibility and low cost. NeuroRighter allows 64 channels of stimulation and recording for around US $10,000, along with the ability to integrate with other software and hardware. Here, we present substantial enhancements to the NeuroRighter platform, including a redesigned desktop application, a new stimulation subsystem allowing arbitrary stimulation patterns, low-latency data servers for accessing data streams, and a new application programming interface (API) for creating closed-loop protocols that can be inserted into NeuroRighter as plugin programs. This greatly simplifies the design of sophisticated real-time experiments without sacrificing the power and speed of a compiled programming language. Here we present a detailed description of NeuroRighter as a stand-alone application, its plugin API, and an extensive set of case studies that highlight the system’s abilities for conducting closed-loop, multichannel interfacing experiments.

High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.