Objective. Most neuroprosthetic implants employ pulsatile square-wave electrical stimuli, which are significantly different from physiological inter-neuronal communication. In case of retinal neuroprosthetics, which use a certain type of pulsatile stimuli, reliable object and contrast discrimination by implanted blind patients remained challenging. Here we investigated to what extent simple objects can be discriminated from the output of retinal ganglion cells (RGCs) upon sinusoidal stimulation. Approach. Spatially confined objects were formed by different combinations of 1024 stimulating microelectrodes. The RGC activity in the ex vivo retina of photoreceptor-degenerated mouse, of healthy mouse or of primate was recorded simultaneously using an interleaved recording microelectrode array implemented in a CMOS-based chip. Main results. We report that application of sinusoidal electrical stimuli (40 Hz) in epiretinal configuration instantaneously and reliably modulates the RGC activity in spatially confined areas at low stimulation threshold charge densities (40 nC mm−2). Classification of overlapping but spatially displaced objects (1° separation) was achieved by distinct spiking activity of selected RGCs. A classifier (regularized logistic regression) discriminated spatially displaced objects (size: 5.5° or 3.5°) with high accuracy (90% or 62%). Stimulation with low artificial contrast (10%) encoded by different stimulus amplitudes generated RGC activity, which was classified with an accuracy of 80% for large objects (5.5°). Significance. We conclude that time-continuous smooth-wave stimulation provides robust, localized neuronal activation in photoreceptor-degenerated retina, which may enable future artificial vision at high temporal, spatial and contrast resolution.

Event Abstract Back to Event Lossless Compression of Neural Signals with Predictor Schemes Achieving more than fivefold data Reduction of in-vitro Recorded Retinal Signals Matteo Pagin1*, Florian Jetter2, Günther Zeck2 and Maurits Ortmanns1 1 Universität Ulm, Institut für Mikroelektronik, Germany 2 Natural and Medical Sciences Institute, Germany Motivation: State-of-the art CMOS-based in-vitro microelectrode arrays feature thousands of recording channels. In combination with the recording bandwidth required for action potential identification they generate of huge amount of data. A system employing 65536 channels [1] at a sampling rate of 10kHz and 12 bit resolution generates about 983Mbyte/s data rate. Similar data rates are obtained for the CMOS based MEA used here which employs 4225 channels sampled at 25 kHz at a resolution of 14 bit [2].Storage of such a huge amount of data can greatly benefit from lossless compression which reduces the data without any loss of information. A predictor based scheme was proposed in [3] and successfully used to compress pre-recorded data from neural implants. In this abstract the scheme is extensively tested on in-vitro retinal signals recorded from 4225 channels [2]. Material and Methods: 1) Predictor based compression: This scheme, also known as predictive or differential encoding, consists in using a predictor block to forecast the incoming sample of a signal using past information about that signal. After prediction the error is calculated by subtracting the true signal value and the predicted one. The distribution of the error presents a lower dynamic range and a more skewed distribution than the original signal. These conditions can then be exploited by an entropy encoder to effectively compress the signal (Figure 1). The compressed error is stored along with the predictor and can be used later for signal reconstruction. The compression is hence lossless since it is possible, using the saved predictor, to reconstruct exactly the original signal. 2) Predictor implementation: In this work a one-layer linear neural network is used to predict the incoming signal [3]. The neural network consists then of one neuron which makes its prediction by multiplying past values of the signal by a learned set of weights and summing them together. The weights are learned from a short segment of neural signal (~ 2000 samples) using a Levenberg–Marquardt algorithm which minimizes the least mean squared error between the original signal and the predicted value. The past values of the signal can be either samples coming from the same channel to be predicted or from adjacent channels, thus allowing the neural network to exploit spatial and temporal redundancy present in the recording. 3) Evaluation Metric: To assess the performance of the compression algorithm the measure of compress ratio is used here and defined as the number of bits required before the compression divided by the number of bits used after compression. Since compression is lossless there is no need to quantify information losses. Results and Discussion: The compression technique is extensively tested on a CMOS-based MEA of 65x65 channels of in-vitro recorded retinal ganglion cell spikes. Compress ratios for each channel is shown in Figure 2. An average data reduction of 5.6 is achieved with a standard deviation of approximately 0.28; demonstrating that the compression can effectively reduce the data rate and is overall quite stable over the whole array. The channels of one row (#57) appeared to be broken in the recording. It is possible to see that here the compression is worse than the average. Using spatial information combined with temporal information in this dataset does not provide an advantage resulting in an average compression of 5.58, which is only slightly worse than compression per single channel. Figure 1 Figure 2 References [1] David Tsai, Daniel Sawyer, Adrian Bradd, Rafael Yuste & Kenneth L. Shepard. A very large-scale microelectrode array for cellular- resolution electrophysiology. Nature Communications 8, 1802 (2017) [2] Bertotti G., Velychko D. et al., A CMOS-based sensor array for in-vitro neural tissue interfacing with 4225 recording sites and 1024 stimulation sites, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) [3] Pagin, M.; Ortmanns, M., A Neural Data Lossless Compression Scheme Based on Spatial and Temporal Prediction, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) Keywords: Neural signal compression, predictive coding, mea array compression, data recuction, neural signal processing Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018. Presentation Type: Poster Presentation Topic: Microelectrode Array Technology Citation: Pagin M, Jetter F, Zeck G and Ortmanns M (2019). Lossless Compression of Neural Signals with Predictor Schemes Achieving more than fivefold data Reduction of in-vitro Recorded Retinal Signals. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00034 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Mar 2018; Published Online: 17 Jan 2019. * Correspondence: Mr. Matteo Pagin, Universität Ulm, Institut für Mikroelektronik, Ulm, Germany, matteo.pagin@uni-ulm.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Matteo Pagin Florian Jetter Günther Zeck Maurits Ortmanns Google Matteo Pagin Florian Jetter Günther Zeck Maurits Ortmanns Google Scholar Matteo Pagin Florian Jetter Günther Zeck Maurits Ortmanns PubMed Matteo Pagin Florian Jetter Günther Zeck Maurits Ortmanns Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

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.

The effect of optical stimulation of neural tissue is considered in capacitively coupled CMOS micro-electrode arrays used for in vitro extracellular recording from neural tissue. Using a 25-nm high-k TiO2 sensor dielectric with 20% ZrO2, light-induced currents through the dielectric are found at short wavelengths within the visible and relevant spectrum for the above-mentioned purpose. Purely capacitive behavior is obtained for green light and longer wavelength, leakage-induced artifacts at shorter wavelengths are avoided by using optimized operating conditions of recording sites and entire system.

Electrical imaging of extracellular potentials reveals the activity of electrogenic cells and of networks thereof over several orders of magnitude, both in space and time. On a spatial scale, electrical activity propagates in nanometer-sized nerve fibers (axons, dendrites), which connect cells in a biological network over several millimeters. On a temporal scale, changes of the extracellular potential caused by action potentials occur on a sub-millisecond scale, while network activity may be modulated over seconds. Here, different electrode arrays are described, which are designed to image modulations of the electrical potentials over a wide spatiotemporal range. In the second part, typical applications and scientific questions in neuroscience research addressed so far are reviewed. The review ends with an outlook on expected developments.

Event Abstract Back to Event Wavelength-sensitivity of mouse retinal ganglion cells recorded by a high-density micro electrode array (MEA) Florian Jetter1, Gabriel Bertotti2, Roland Thewes3 and Günther Zeck4* 1 NMI at the University Tuebingen, Neurochip Research, Germany 2 Technische Universität Berlin, Chair of Sensor and Actuator Systems, Germany 3 Technische Universität Berlin, Chair of Sensor and Actuator Systems, Germany 4 NMI at the University Tuebingen, Neurochip Research, Germany Motivation: High-density CMOS-MEAs can be used to simultaneously record the electrical spiking activity from hundreds of neurons [Bertotti et al., 2014]. Neuronal spiking can be induced by electrical stimulation [Eickenscheidt and Zeck 2014], by light stimulation applied to a light-sensitive retina [Zeck et al. 2011], or by light stimulation of optogenetically transfected neurons [Herrmann et al. 2014]. In this work, we investigate the wavelength-sensitivity of different mouse retinal ganglion cells and the intrinsic wavelength-sensitivity of the response of high-density CMOS-MEAs. Material and Methods: A CMOS-based high-density MEA comprising 4225 recording sites is used for recording the ganglion cell activity in C57/Bl6 mouse retina during flickering light stimulation (1 and 5 Hz, respectively) with different wavelength and of different light stimulus sizes. Light stimuli presented on a DMD (µ-matrix, Rapp Optoelectronic, Germany) are focused through a microscope objective on the retina. The DMD is illuminated by an LED system commonly used for optogenetic activation (pe-4000, coolLED, UK). Here the results for four stimulation wavelengths are presented (405, 470, 525, and 635nm) at intensities as high as 2 mW/mm² (470 nm). Recorded data is sampled at 25 kHz. Results: Light stimulation (stimulus area: 1 mm2) evokes spiking in the interfaced retina. Based on the stimulus polarity retinal ganglion cells are broadly classified in ON or OFF type, depending on whether they respond to light on- or offset, respectively. In Fig. 1A filtered recordings are shown of the measured extracellular voltage from two selected sensors during 1 Hz stimulation. Both, ON and OFF cell types respond to the three highest wavelengths used here. The sensor site recording the ON transient cell also detects activity from a second OFF cell with smaller amplitude. In Fig.1B we present the unfiltered extracellular voltage traces recorded by a third selected sensor site for all four wavelengths. Light onset leads to a measurable change of the current in the sensing transistor (cf. Bertotti et al., 2014) or more generally speaking of the sensed recording site response, reflected as a low-frequency change of signal back-converted into the voltage domain. However, this does not prevent the detection of light-induced spiking. We note, that the ganglion cell shown in Fig. 1B is not activated by the 635 nm light stimulus. Ongoing experiments investigate the sensitivity of retinal ganglion cells to chromatic stimulation and to stimuli presented at various intensities. The induced variations of the sensor signals are wavelength-dependent, with the highest change obtained for the lowest wavelength (405 nm) and undetectable changes for red light (635 nm). Conclusion: It is shown that recording of cellular spiking activity with CMOS-based MEAs is possible during optical stimulation. Ganglion cells in the mouse retina have different wavelength-sensitivities. The light-induced low-frequency drift of the sensor signals can be completely removed for the tested wavelengths and intensities using high-pass filtering. References: [1] Bertotti G. et al., Proc. IEEE BioCAS, 2014, DOI: 10.1109/BioCAS.2014.6981723 [2] Eickenscheidt M. and Zeck G., J.Neural Eng. 2014, 11(3):036006, DOI:10.1088/1741-2560/11/3/036006 [3] Zeck G. et al., PLoS One, 2011, 6(6):e20810, DOI: 10.1371/journal.pone.0020810 [4] Herrmann T. et al., 2014, Proc. of the 9th Int. Meeting on Substrate-Integrated Microelectrodes, Reutlingen, Germany Figure legend: Light-stimulated ganglion cell activity from mouse retina recorded at four different wavelengths. White segments illustrate time intervals of illumination while grey segments resemble intervals without illumination. (A):Light induced spiking in an OFF sustained ganglion cell type and an ON transient ganglion cell to the same stimulus. The stimulus wavelength used is given in the right column. Data are band-pass filtered (200 – 3000 Hz). (B) Unfiltered extracellular voltage traces showing the induced activity in ON sustained retinal ganglion cells. A slow drift of the sensor signal is visible, which does not prevent detection of ganglion cell spiking. Figure 1 Acknowledgements This work was supported by a grant of the Federal Ministry for Education and Research /BMBF (FKZ 031L0059) Keywords: CMOS-MEA, ganglion cell, Optical stimulation, Mouse Retina Conference: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016. Presentation Type: Poster Presentation Topic: MEA Meeting 2016 Citation: Jetter F, Bertotti G, Thewes R and Zeck G (2016). Wavelength-sensitivity of mouse retinal ganglion cells recorded by a high-density micro electrode array (MEA). Front. Neurosci. Conference Abstract: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays. doi: 10.3389/conf.fnins.2016.93.00097 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Jun 2016; Published Online: 24 Jun 2016. * Correspondence: Dr. Günther Zeck, NMI at the University Tuebingen, Neurochip Research, Reutlingen, Germany, guenther.zeck@tuwien.ac.at Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Florian Jetter Gabriel Bertotti Roland Thewes Günther Zeck Google Florian Jetter Gabriel Bertotti Roland Thewes Günther Zeck Google Scholar Florian Jetter Gabriel Bertotti Roland Thewes Günther Zeck PubMed Florian Jetter Gabriel Bertotti Roland Thewes Günther Zeck Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Understanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a “template-finding” phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a “template-matching” phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain “ground truth” data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings.

A CMOS MEA with 4k recording and 1k stimulation sites is used for time-continuous recording of neural signals during stimulation. All sites consist of thin metal electrodes on the chip surface covered by a thin high-k dielectric. Recording and stimulation channels are electrically separated but physically superimposed so that recording at the site of stimulation is possible as well as at any other location within the array. A compensation method is introduced to fully compensate for stimulation signal-induced artifacts in the recording channels. Measurement results reveal the feasibility of our approach.