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High-density CMOS-based Multielectrode Arrays (MEA) provide thousands of channels to record extracellular electrical activity of neuronal networks. Such a high channels count generates an amount of data that is difficult to manage with long-term fully implantable neural interfaces, where power and data transmission are provided wirelessly. To overcome this limitation, a low resources digital signal processor able to reduce the amount of transmitted data to a relevant subset represented by the spiking activity is essential. Unfortunately, the resources required to detect spikes linearly grow with the number of channels, limiting the total amount of MEA pixels in these devices. This work presents a method, here called Spike Detection-by-Difference (SDD), to drastically reduce this limit, for real-time spike detection with an impact on resources and consumption independent from the total channels count. It exploits the high resolution of MEAs to separate the highly localized extracellular action potential from the large-scale local field potential. The SDD is compared with the standard spike detection approaches as the Threshold Crossing (TC) and the Nonlinear Energy Operator (NEO). The detection accuracy is compared for different spiking amplitudes on a synthetic dataset generated from real recordings, showing a detection accuracy of 90% for spikes as low as 45 µV, with a noise in the frequency band from 300 Hz to 5 kHz of 10 µVrms. Furthermore, the resource consumption shows a reduction of the 91,5% compared to the TC and of 94% compared to the NEO on a 32x32 pixels matrix. This reduction can be further accentuated increasing the matrix size or reducing the number of columns.

Understanding brain signals as an outcome of brain’s information processing is a challenge for the neuroscience and neuroengineering community. Rodents sense and explore the environment through whisking. The local field potentials (LFPs) recorded from the barrel 28 columns of the rat somatosensory cortex (S1) during whisking provide information about the tactile information processing pathway. Particularly when using large-scale high-resolution neuronal probes, during each experiment many single LFPs are recorded as an outcome of underlying neuronal network activation and averaged to extract information. However, single LFP signals are frequently very different from each other and extracting information provided by their shape is a useful way to better decode information transmitted by the network. In this work, we propose an automated method capable of classifying these signals based on their shapes. We used template matching approach to recognize single LFPs and extracted the contour information from the recognized signal to generate a feature matrix, which is then classified using the intelligent K–means clustering. As an application example, shape specific information (e.g., latency, and amplitude) of LFPs evoked in the rat somatosensory barrel cortex and used in decoding the rat whiskers information processing pathway is provided by the method.

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

This work presents a comparison between different neural spike algorithms to find the optimum for in vivo implanted EOSFET (electrolyte&ndash, oxide-semiconductor field effect transistor) sensors. EOSFET arrays are planar sensors capable of sensing the electrical activity of nearby neuron populations in both in vitro cultures and in vivo experiments. They are characterized by a high cell-like resolution and low invasiveness compared to probes with passive electrodes, but exhibit a higher noise power that requires ad hoc spike detection algorithms to detect relevant biological activity. Algorithms for implanted devices require good detection accuracy performance and low power consumption due to the limited power budget of implanted devices. A figure of merit (FoM) based on accuracy and resource consumption is presented and used to compare different algorithms present in the literature, such as the smoothed nonlinear energy operator and correlation-based algorithms. A multi transistor array (MTA) sensor of 7 honeycomb pixels of a 30 &mu, m2 area is simulated, generating a signal with Neurocube. This signal is then used to validate the algorithms&rsquo, performances. The results allow us to numerically determine which is the most efficient algorithm in the case of power constraint in implantable devices and to characterize its performance in terms of accuracy and resource usage.

This paper presents a Real-Time Neural Spikes (RT-Neu) Imaging system on FPGA that processes and detects the electrical activity of a neurons population taken from rat hippocampi on an Electrolyte-Oxide-Semiconductor (EOS) Multi Electrode Array (MEA) local matrix of 32×32 pixels. RT-Neu has been implemented on Xilinx Zynq-7000 ARM/FPGA SoC. It receives the neural signals coming from a 9.375 kSample/(sec·pixel) 32×32 pixels EOS Biosensor, filters the single-pixel low-frequency offset/noise components and finally performs a multi-pixel signal processing (using a PCA-based correlation algorithm) to provide a final spatial map of the neural culture electrical activity. The correlation algorithm has been implemented to operate on multiplexed signals allowing to identify single neural Action Potentials (AP) with amplitudes as low as 215 μV 0-PEAK . A dedicated GUI has been developed to monitor in real-time the neuron population electrical activity and whose demo video can be found at [1].

This paper presents an FPGA implementation of a DSP performing real time spike detection on the electrical activity of an in vitro neuronal culture of rat hippocampi. The DSP enhances the Signal-to-noise ratio (SNR) of samples recorded by a 1024 pixels Multi Transistor Array (MTA) at 9375 Samples/Sec per pixel of $\sim 6\ \boldsymbol{\mu}\mathbf{m}$ pitch. The implementation integrates in the same system a Time Division Multiplexing (TDM) filter and a spatio-temporal correlation algorithm, to increase the SNR up to identify spikes as low as $215\ \boldsymbol{\mu}\mathbf{V}_{0-\mathbf{PEAK}}$ . The digital filter is a 2nd order high pass Infinite input response (IIR) Chebyshev filter. The spatio-temporal correlation exploits the MTA smaller pixels size and the high sample-rate to compute an equivalent pixel from a group of 7 pixels and 3 consecutive frames for a total of 21 samples and it is supported by a multi-channel noise power estimation. Finally, this paper shows the results achieved on the performed experiments and compares the system with others experiments using different sensors and algorithms.

This paper presents the result of a multidisciplinary experiment where electrical activity from a cultured rat hippocampi neuronal population is detected in real time by a FPGA implemented digital circuit. State-of-the-art EOMOSFET Multi Electrode Array (MEA) biosensors exploits a capacitive coupling between the biological environment and the sensing electronics to minimize invasiveness and cell damage, at the price of a lower SNR. For this reason, they are typically improved by noise rejection algorithms. Real time neural spikes detection opens unthinkable scenarios, allowing to stimulate single neurons in response to their behavior, possibly improving medical conditions like epilepsy. In this scenario, a spike sorting algorithm has been hardware implemented, allowing real time neural spike detection with a latency of 165ns.

This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are characterized by low signal-to-noise ratios caused by both the contactless sensing of weak extracellular voltages and the high noise power coming from cells and analog electronics signal processing. This low SNR forces to utilize advanced noise rejection algorithms to separate relevant neural activity from noise, which are usually implemented via software/off-line. However, off-line detection of neural spikes cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA-based system capable to detect in real-time neural spikes from background noise. The output signals of the proposed system provide real-time spatial and temporal information about the culture electrical activity and the noise power distribution with a minimum latency of 165 ns. The output bit-stream can be further utilized to detect synchronous activity within the neural network.

This paper presents the results of a multidisciplinary experiment where the electrical activity of a rat hippocampus cultured neurons population has been detected and mapped by an advanced FPGA spike-sorting algorithm. Neurons are growth over a silicon chip that is thus capacitively coupled with neuronal cells. Due to noise power coming from bio-silicon interface and analog electronics signal processing, the Action Potentials detection intrinsically needs advanced noise rejection algorithms which are often software/off-line implemented. This approach disables instantaneous detection of neural spikes and cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA system able to separate relevant neuronal cells potentials from noise. The FPGA output signals provide real time spatial mapping of biosensor electrical activity, noise and synchronous neural network activity.

The features of implant devices and the reactions of bone-derived cells to foreign surfaces determine implant success during osseointegration. In an attempt to better understand the mechanisms underlying osteoblasts attachment and spreading, in this study adhesive peptides containing the fibronectin sequence motif for integrin binding (Arg-Gly-Asp, RGD) or mapping the human vitronectin protein (HVP) were grafted on glass and titanium surfaces with or without chemically induced controlled immobilization. As shown by total internal reflection fluorescence microscopy, human osteoblasts develop adhesion patches only on specifically immobilized peptides. Indeed, cells quickly develop focal adhesions on RGD-grafted surfaces, while HVP peptide promotes filopodia, structures involved in cellular spreading. As indicated by immunocytochemistry and quantitative polymerase chain reaction, focal adhesions kinase activation is delayed on HVP peptides with respect to RGD while an osteogenic phenotypic response appears within 24 h on osteoblasts cultured on both peptides. Cellular pathways underlying osteoblasts attachment are, however, different. As demonstrated by adhesion blocking assays, integrins are mainly involved in osteoblast adhesion to RGD peptide, while HVP selects osteoblasts for attachment through proteoglycan-mediated interactions. Thus an interfacial layer of an endosseous device grafted with specifically immobilized HVP peptide not only selects the attachment and supports differentiation of osteoblasts but also promotes cellular migration.

In this paper we investigated atomic layer deposition (ALD) TiO2 thin films deposited on implantable neurochips based on electrolyte-oxide-semiconductor (EOS) junctions, implementing both efficient capacitive neuron-silicon coupling and biocompatibility for long-term implantable functionality. The ALD process was performed at 295 degrees C using titanium tetraisopropoxide and ozone as precursors on needle-shaped silicon substrates. Engineering of the capacitance of the EOS junctions introducing a thin Al2O3 buffer layer between TiO2 and silicon resulted in a further increase of the specific capacitance. Biocompatibility for long-term implantable neuroprosthetic systems was checked upon in-vitro treatment. (C) 2011 Elsevier B.V. All rights reserved.

Introduction: Anti-glutamic acid decarboxylase autoantibodies (GAD-Ab) are commonly considered the marker of autoimmune diabetes; they were first described in patients affected by stiff-person syndrome and recently, in ataxic or epileptic patients. The pathogenetic role of GAD-Ab remains unclear but inhibition of GABA synthesis or interference with GABA exocytosis are hypothesized. The aim of the study was to assess whether GAD-Ab interfere with neuronal transmission. Patients and methods: Serum from a GAD-Ab positive epileptic patient (by IHC and RIA), serum from a GAD-positive (only by RIA) diabetic case, sera from two epileptic GAD-Ab negative patients and a normal control were selected. Post-synaptic inhibitory potentials (IPSPs) were registered on hippocampal neurons in culture before and after the application of diluted sera in a patch clamp study. Results: A significant increase in the frequency of IPSPs was observed after application of GAD-positive epileptic serum, while no effect was noted using sera from negative controls. Conclusion: The inhibition in neuronal transmission only after application of GAD-positive epileptic serum, suggests an interference with GABA function and consequently with neuronal inhibition supporting a pathogenetic role of GAD-Ab in the development of epilepsy.

Immunological derangement is assumed to be present in a subgroup of patients affected by drug-resistant epilepsy with serum harboring antiglutamic acid decarboxylase autoantibodies (GAD-Ab). To further investigate the specific reactivity of GAD-Ab with target cells, we tested sera from drug-resistant epileptics harboring GAD-Ab on cultured fetal rat hippocampal neurons. As a control, we tested sera from GAD-Ab-negative epileptics and GAD-Ab-positive patients affected by Stiff Person Syndrome (SPS), ataxia or diabetes. A specific pattern of reactivity, varying according to disease, was detected on application of sera from GAD-Ab-positive patients with epilepsy, SPS and ataxia, but no specific labeling was found on application of sera from patients with GAD-Ab-negative epilepsy or from GAD-Ab-positive diabetic controls. (C) 2006 Elsevier Inc. All rights reserved.