A 256-input micro-electrode array with integrated cmos amplifiers for neural signal recording

Thesis (Ph.D.)--Boston UniversityThe nervous system communicates and processes information through its basic structural units -- individual neurons (nerve cells). Neurons convey neural information via electrical and chemical signals, which makes electrophysiological recording techniques very important in the study of neurophysiology. Specifically, active microelectrode arrays (MEAs) with amplifiers integrated on the same substrate are used because they provide a very powerful neural electrical recording technique that can be directly interfaced to acute slices and cell cultures. 2D planer electrodes are typically used for recording from neural cultures in vitro, while in vivo recording in live animals invariably requires the use of 3D electrodes. I have designed an active MEA with neural amplifiers and 3D electrodes, all integrated on a single chip. The electrodes are commercially available 3D C4 (Controlled Collapse Chip Connect) flip-chip bonding solder balls that have a diameter of 100 µm and a pitch of 200 µm. An active MEA neural recording chip -- the Multiple-Input Neural Sensor (MINS) chip -- was designed and fabricated using the IBM BiCMOS 8HP 0.13 µm technology. The MINS IC has 256 input channels that are time-division multiplexed into two output pads. Each channel was designed to work at a 20 kHz frame rate with a total voltage gain of 60 dB per channel with an input-referred noise voltage of 5.3 µVrms over 10 Hz to 10 kHz. The entire MINS chip has an area of 4 x 4 mm^2 with 256 input C4s plus 20 wire-bond pads on two adjacent edges of the chip for power, control, and outputs. The fabricated MINS chips are wire-bonded to standard pin grid array (PGA), open-top PGA, and custom-designed printed circuit board (PCB) packages for electrical, in vitro, and in vivo testing, respectively. After process variation correction, the voltage gain of the 256 neural amplifiers, measured in vitro across several chips, has a mean value of 58.7 dB and a standard deviation of 0.37 dB. Measurements done with the electrical testing package demonstrate that the MINS IC has a flat frequency response from 0.05 Hz to 1.4 MHz, an input-referred noise voltage of 4.6 µVrms over 10 Hz to 10 kHz, an output voltage swing as large as 1.5 V peak-to-peak, and a total power consumption of 11.25 mW, or 43.9 µW per input channel

Locally embedded presages of global network bursts

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks

Extracellular stimulation system for the modification of network parameters in cultured neural networks

Este proyecto se centra en el uso de dispositivos de microelectrodos MEAs (Multi Electrode Arrays) de última generación para el estudio y la manipulación de redes neuronales en cultivo. Chips MEA, con 26400 electrodos situados en una superficie de 3.85x2.10mm^2, fueron utilizados para registrar la actividad eléctrica de dos cultivos de neuronas corticales disociadas obtenidas de embriones de rata. En las mismas plataformas MEA, se implementó un protocolo de estimulación en bucle cerrado, de manera que se pudieran enviar pulsos eléctricos de estimulación a determinados electrodos en respues a potenciales de acción detectados en otro electrodo. Uno de los cultivos de neuronas fue sometido al protocolo de estimulación en bucle cerrado mientras que el segundo cultivo fue utilizado como control. Se desarrollaron diferentes métodos con el fin de hacer una caracterización funcional de los cultivos. El análisis funcional de los registros obtenidos en los experimentos indican que la estimulación en bucle cerrado provocó perdidas significativas y generalizadas de actividad y conectividad en la red neuronal en cultivo

A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

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