The EcoChip : a wireless multi-sensor platform for comprehensive environmental monitoring

This paper presents the EcoChip, a new system based on state-of-the-art electro-chemical impedance (EIS) technologies allowing the growth of single strain organisms isolated from northern habitats. This portable system is a complete and autonomous wireless platform designed to monitor and cultivate microorganisms directly sampled from their natural environment, particularly from harsh northern environments. Using 96-well plates, the EcoChip can be used in the field for realtime monitoring of bacterial growth. Manufactured with highquality electronic components, this new EIS monitoring system is designed to function at a low excitation voltage signal to avoid damaging the cultured cells. The high-precision calibration network leads to high-precision results, even in the most limiting contexts. Luminosity, humidity and temperature can also be monitored with the addition of appropriate sensors. Access to robust data storage systems and power supplies is an obvious limitation for northern research. That is why the EcoChip is equipped with a flash memory that can store data over long periods of time. To resolve the power issue, a low-power microcontroller and a power management unit control and supply all electronic building blocks. Data stored in the EcoChip’s flash memory can be transmitted through a transceiver whenever a receiver is located within the functional transmission range. In this paper, we present the measured performance of the system, along with results from laboratory tests in-vitro and from two field tests. The EcoChip has been utilized to collect bio-environemental data in the field from the northern soils and ecosystems of Kuujjuarapik and Puvirnituq, during two expeditions, in 2017 and 2018, respectively. We show that the EcoChip can effectively carry out EIS analyses over an excitation frequency ranging from 750 Hz to 10 kHz with an accuracy of 2.35%. The overall power consumption of the system was 140.4 mW in normal operating mode and 81 µW in sleep mode. The proper development of the isolated bacteria was confirmed through DNA sequencing, indicating that bacteria thrive in the EcoChip’s culture wells while the growing conditions are successfully gathered and stored

A 4.8-μVrms-Noise CMOS-Microelectrode Array With Density-Scalable Active Readout Pixels via Disaggregated Differential Amplifier Implementation

We demonstrate a 4.8-μVrms noise microelectrode array (MEA) based on the complementary-metal-oxide-semiconductor active-pixel-sensors readout technique with disaggregated differential amplifier implementation. The circuit elements of the differential amplifier are divided into a readout pixel, a reference pixel, and a column circuit. This disaggregation contributes to the small area of the readout pixel, which is less than 81 μm2. We observed neuron signals around 100 μV with 432 electrodes in a fabricated prototype chip. The implementation has technological feasibility of up to 12-μm-pitch electrode density and 6,912 readout channels for high-spatial resolution mapping of neuron network activity

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

A 16384-electrode 1024-channel multimodal CMOS MEA for high-throughput intracellular action potential measurements and impedance spectroscopy in drug-screening applications

Patch clamp is currently the gold standard for studying cell electrophysiology in preclinical drug discovery. Since it is a time-consuming technique, passive and active multielectrode arrays (MEAs) have been introduced for increased throughput in extracellular (ExC) in vitro measurements [1-3]. However, most of these tools cannot capture the essential features of intracellular (InC) action potentials (APs) used for studying drug toxicity. It has been shown that InC access can be achieved by highly localized electroporation [4], which allows low-impedance electrical recording of the InC voltage. While this technique was explored in recent designs [4-5], a tool that enables high-throughput InC measurements is not yet available. Impedance measurement is also used to study cell morphology, adhesion, differentiation and contractility [6]. Recent designs [1-3] already include impedance spectroscopy (IS), but their methods are not fast enough to capture in detail the contractile activity of cardiomyocytes. Since some drugs can inhibit cell beating without affecting the APs [6], ExC/InC recording and impedance measurement are complementary