Recording many neurons for a long time: The ultimate aim of chronic recordings is to sample from the same neuron over days and weeks. However, this goal has been difficult to achieve for large populations of neurons. Steinmetz et al. describe the development and testing of Neuropixels 2.0. This new electrophysiological recording tool is a miniaturized, high-density probe for both acute and long-term experiments combined with sophisticated software algorithms for fully automatic post hoc computational stabilization. The technique also provides a strategy for extending the number of recorded sites beyond the number of available recording channels. In freely moving animals, extremely large numbers of individual neurons could thus be followed and tracked with the same probe for weeks and occasionally months. Science , this issue p. eabf4588

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 mm ² ) implements dual-band recording and a 171.6 Mbps digital interface. Measurement results show a total input-referred noise of 6.4 μV _rms and a total power consumption of 49.1 μW/channel.

We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission).

Multi-electrode arrays (MEAs) are a candidate technology to screen cardiotoxicity in vitro because they enable noninvasive recording of cardiac beating rate, electrical field potential duration, and other parameters. In this paper, we present an active MEA chip featuring 16 384 electrodes, 1024 simultaneous readout channels, and 64 stimulation units (SUs) to enable six different cell-interfacing modalities: extracellular and intracellular recording, current and voltage stimulation, fast impedance motoring, and impedance spectroscopy (IS). The chip was designed and fabricated in a 0.13- $\mu \text{m}$ Al CMOS technology, and the electrodes were fabricated on top of the silicon substrate using biocompatible TiN. Measurement results show a total input-referred noise of $7.5 \pm 0.6~\mu \text{V}_{\mathrm{rms}}$ and $12.0 \pm 2.4~\mu \text{V}_{\mathrm {rms}}$ for the action potential and full bands, respectively, with a total power consumption of 95 mW for 1024 readout channels. We have experimentally validated the CMOS MEA in in vitro experiments, demonstrating all the different modalities. This novel platform will potentially enable high-throughput electrical activity monitoring and drug screening of cardiomyocytes.

Large-scale $in\ vivo$ electrophysiology requires tools that enable simultaneous recording of multiple brain regions at single-neuron level. This calls for the design of more compact neural probes that offer even larger arrays of addressable sites and high channel counts. With this aim, we present in this paper a quad-shank approach to integrate as many as 5,120 sites on a single probe. Compact fully-differential recording channels were designed using a single-gain-stage neural amplifier with a 14-bit ADC, achieving a mean input-referred noise of 7.44 μ $\text{V}_{\text{rms}}$ in the action-potential band and 7.65 μ $\text{V}_{\text{rms}}$ in the local-field-potential band, a mean total harmonic distortion of 0.17% at 1 kHz and a mean input-referred offset of 169 μV. The probe base incorporates 384 channels with on-chip power management, reference-voltage generation and digital control, thus achieving the highest level of integration in a neural probe and excellent channel-to-channel uniformity. Therefore, no calibration or external circuitry are required to achieve the above-mentioned performance. With a total area of $2.2 \times 8.67\text{ mm}^2$ and a power consumption of 36.5 mW, the presented probe enables full-system miniaturization for acute or chronic use in small rodents.

Memristors show great potential for being integrated into CMOS technology and provide new approaches for designing computing-in-memory (CIM) systems, brain-inspired applications, trimming circuits and other topologies for the beyond-CMOS era. A crucial characteristic of the memristor is multi-state ¹ switching. Memristors are capable of representing information in an ultra-compact fashion, by storing multiple bits per device. However, certain challenges remain in multi-state memristive circuits and systems design such as device stability and peripheral circuit complexity. In this paper, we review the state of the art of multi-state memristor technologies and their associated CMOS/Memristor circuit design, and discuss the challenges regarding device imperfection factors, modelling, peripheral circuit design and layout. We present measurement results of our in-house fabricated multi-state memristor as an example to further illustrate the feasibility of applying multi-state memristors in CMOS design, and demonstrate their related future applications such as multi-state memristive memories in machine learning, memristive neuromorphic applications, trimming and tuning circuits, etc. In the end, we summarize past and present efforts done in this field and envisage the direction of multi-state memristor related research.