SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings

Abstract Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18  μ m CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28  μ m. Extensive bench tests showed an in-pixel gain of 45.4 ± 0.4 dB (low pass, F-3 dB = 4 kHz), an input referred noise of 7.5 ± 0.67 μVRMS (300 Hz to 7.5 kHz) and a power consumptio

Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics

Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR

A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals

A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays

Modeling, Evaluation, and Comparison of CRZ and RSD Redundant Architectures for Two-Step A/D Converters

Redundancy in the output code as an instrument to reduce the impact of nonidealities in different architectures of two-step analog to digital converters (ADC) is investigated. The CRZ converters, which are the converters obtained from the conventional CR architecture with addition of Z extra decision levels, are formalized and compared with the RSD converter. Two distinct models are proposed to investigate separately, by means of system level simulations, the impact of each source of error: the behavioral model and the circuit model. The first one is intended to give an intuitive understanding of how redundancy improves the output response of this class of converters, while the latter is introduced to find, at an early design stage, the optimum sizing of passive components meeting a given resolution. Simulation results from both models are discussed and the different performances of the various converters are evaluated with respect to power, area, and effective resolution

Biologically Compatible Neural Interface To Safely Couple Nanocoated Electrodes to the Surface of the Brain

The ongoing interest in densely packed miniaturized electrode arrays for high-resolution epicortical recordings has induced many researchers to explore the use of nanomaterial coatings to reduce electrode impedance while increasing signal-to-noise ratio and charge injection capability. Although these materials are very effective, their use in clinical practice is strongly inhibited by concerns about the potential risks derived from the use of nanomaterials in direct contact with the human brain. In this work we propose a novel approach to safely couple nanocoated electrodes to the brain surface by encapsulating them with a biocompatible hydrogel. We prove that fibrin hydrogel coating over nanocoated high-density arrays of epicortical microelectrodes is electrically transparent and allows avoiding direct exposure of the brain tissue to the nanocoatings while maintaining all the advantages derived from the nanostructured electrode surface. This method may make available acute and sub-acute neural recordings with nanocoated high-resolution arrays for clinical applications

A closed-loop system for neural networks analysis through high density MEAs

In this work we present a FPGA-based system for real-time processing of neural signals acquired by commercial high-density microelectrode array (HDMEA). The considered MEA features 4096 electrodes with 18kHz sampling frequency and 12-bit resolution, thus produces nearly 1 Gbps of data. Within the implementation, we considered low-latency as a main objective, to allow for closed-loop acquisition-stimulation experiments, that represent a novel promising frontier in neuro-physiology and in the development of brain-machine interfaces. The developed platform is implemented on a low-to-mid Zynq all-programmable SoC, and is able to perform all the required computation (from signal acquisition to response generation) with less than 2ms latency, enabling closed-loop applications in a wide range of experiments

Carbon nanotube composite coating of neural microelectrodes preferentially improves the multiunit signal-to-noise ratio

Extracellular metal microelectrodes are widely used to record single neuron activity in vivo. However, their signal-to-noise ratio (SNR) is often far from optimal due to their high impedance value. It has been recently reported that carbon nanotube (CNT) coatings may decrease microelectrode impedance, thus improving their performance. To tease out the different contributions to SNR of CNT-coated microelectrodes we carried out impedance and noise spectroscopy measurements of platinum/tungsten microelectrodes coated with a polypyrrole-CNT composite. Neuronal signals were recorded in vivo from rat cortex by employing tetrodes with two recording sites coated with polypyrrole-CNT and the remaining two left untreated. We found that polypyrrole-CNT coating significantly reduced the microelectrode impedance at all neuronal signal frequencies (from 1 to 10 000 Hz) and induced a significant improvement of the SNR, up to fourfold on average, in the 150-1500 Hz frequency range, largely corresponding to the multiunit frequency band. An equivalent circuit, previously proposed for porous conducting polymer coatings, reproduced the impedance spectra of our coated electrodes but could not explain the frequency dependence of SNR improvement following polypyrrole-CNT coating. This implies that neither the neural signal amplitude, as recorded by a CNT-coated metal microelectrode, nor noise can be fully described by the equivalent circuit model we used here and suggests that a more detailed approach may be needed to better understand the signal propagation at the electrode-solution interface. Finally, the presence of significant noise components that are neither thermal nor electronic makes it difficult to establish a direct relationship between the actual electrode noise and the impedance spectra