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Extracellular voltage fields, produced by a neuron's action potentials, provide a widely used means for studying neuronal and neuronal-network function. The neuron's soma and dendrites are thought to drive the extracellular action potential (EAP) landscape, while the axon's contribution is usually considered less important. However, by recording voltages of single neurons in dissociated rat cortical cultures and Purkinje cells in acute mouse cerebellar slices through hundreds of densely packed electrodes, it is found, instead, that the axon initial segment dominates the measured EAP landscape, and, surprisingly, the soma only contributes to a minor extent. As expected, the recorded dominant signal has negative polarity (charge entering the cell) and initiates at the distal end. Interestingly, signals with positive polarity (charge exiting the cell) occur near some but not all dendritic branches and occur after a delay. Such basic knowledge about which neuronal compartments contribute to the extracellular voltage landscape is important for interpreting results from all electrical readout schemes. Finally, initiation of the electrical activity at the distal end of the axon initial segment (AIS) and subsequent spreading into the axon proper and backward through the proximal AIS toward the soma are confirmed. The corresponding extracellular waveforms across different neuronal compartments could be tracked.

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

A novel system to cultivate and record brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows to continuously record electrical activity of selected individual neurons at high spatial resolution, while monitoring neuronal network activity at the same time. For the first time, properties of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied over four consecutive weeks at daily intervals., 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS), 2015 Transducers, ISBN:978-1-4799-8955-3, ISBN:978-1-4799-8954-6

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., Frontiers in Neuroscience, 9, ISSN:1662-453X, ISSN:1662-4548

Emerging multi-electrode-based brain-machine interfaces (BMIs) and large multi-electrode arrays used in in vitro experiments, enable recording of single neuron's activity on multiple electrodes and allow for an in-depth investigation of neural preparations, even at a sub-cellular level. However, the use of these devices entails stringent area and power consumption constraints for the signal-processing hardware units. In addition, the high autonomy of these units and an ability to automatically adapt to changes in the recorded neural preparations is required. Implementing spike detection in close proximity to recording electrodes offers the advantage of reducing the transmission data bandwidth. By eliminating the need of transmitting the full, redundant recordings of neural activity and by transmitting only the spike waveforms or spike times, significant power savings can be achieved in the majority of cases. Here, we present a low-complexity, unsupervised, adaptable, real-time spike-detection method targeting multi-electrode recording devices and compare this method to other spike-detection methods with regard to complexity and performance.

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

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

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., Frontiers in Neural Circuits, 6, ISSN:1662-5110

The retina is responsible for the processing and encoding of visual stimuli into neural spike trains. The output module of the retina is composed of a single layer of retinal ganglion cells (RGCs) that transmit the encoded information to higher centers in the brain. Using a high-density complementary metal-oxide-semiconductor (CMOS)-based microelectrode array (MEA) to scan the RGC layer, we have shown that it is possible to record and sort the spikes produced by selected RGCs. This technique provides a high spatio-temporal-resolution readout of the neural circuitry and function of the retina.

Emerging CMOS-based microelectrode array devices allow for recording and stimulation of neuronal networks at high spatiotemporal resolution. The assignment of spiking events to individual neurons, a problem referred to as “spike sorting”, is required for the analysis of neuronal recordings. For closed-loop experiments, where a feedback stimulus is applied in response to neuronal spiking patterns, real-time spike sorting techniques are needed. In this paper we demonstrate the potential of using optimal filters in combination with a bidirectional high-density microelectrode array, in order to perform real-time spike sorting also in the presence of overlapping spikes. The high spatial electrode density allows the selection of the electrodes that provide the best filtering performance.

High-density microelectrode arrays (HD-MEAs) with large numbers of densely packed electrodes potentially allow for recording from every cell on the array and generate large, redundant datasets. Blind-source-separation algorithms (BSS), used to separate mixtures of independent sources into the original signals, are an ideal means to be applied to the spike sorting of HD-MEA recordings. We show that recorded neuronal signals represent convoluted mixtures, and we present a BSS algorithm. The algorithm uses the nonlinear energy operator as preprocessor and an extended method of independent-component analysis to separate convoluted mixtures. The algorithm is applied to recordings from retinal ganglion cells, and its performance is evaluated.

We use a planar, CMOS-based microelectrode array (MEA) featuring 3,150 metal electrodes per mm2 and 126 recording channels to record spatially highly resolved extracellular action potentials (EAPs) from Purkinje cells (PCs) in acute cerebellar slices. An Independent-Component-Analysis-based (ICA) spike sorter is used to reveal EAPs of single cells at subcellular resolution. Those EAPs are then used to set up a compartment model of a PC. The model is used to make and finetune estimations of the distance between MEA surface and PC soma. This distance is estimated using the amplitude-independent part of the shape of the EAPs obtained from recordings. The estimation shows that, in our preparations, we can record from PCs with the center of their soma at approximately 35 µm and 90 µm vertical distance to the chip surface.

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 μVrms in the action-potential band (300 Hz-10 kHz) and 5.4 μVrms 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.