Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

Wirelessly powered implants are increasingly being developed to interface with neurons in the brain. They often rely on microelectrode arrays which are limited by their scalability and long-term stability because of their physical size and rigid configuration. Yet some clinical and research applications prioritize a distributed neural interface over one that offers high resolution. One solution to make large scale, fully specifiable, electrical stimulation/recording possible, is to disconnect the electrodes from the base, so that they can be arbitrarily placed, using a syringe, freely in the nervous system. In this work, a wirelessly powered stimulating implant is miniaturized using a novel electrode integration technique, and its implanted depth maximized using innovative optimization design methods for the transmitter and receiver coils. The stimulating device is implemented in a 130nm CMOS technology with the following characteristics: 300 μm × 300 μm × 80 μm size; optimized wireless powering; and integrated circuit, electrodes and coil. The wireless and stimulation capability of the implant is validated in a conductive medium and in-vivo. To the best of our knowledge, the fabricated free-floating miniaturized implant has the best depth-to-volume ratio making it an excellent tool for minimally-invasive scalable neural interface, and thus could eventually replace the rigid arrays that are currently the state-of-the-art in brain set-ups.

While great advances have been made in optimizing fabrication process technologies for solid state image sensors, the need remains to be able to fabricate high quality photosensors in standard CMOS processes. The quality metrics depend on both the pixel architecture and the photosensitive structure. This paper presents a comparison of three photodiode structures in terms of spectral sensitivity, noise and dark current. The three structures are n ⁺ /p-sub, n-well/p-sub and p ⁺ /n-well/p-sub. All structures were fabricated in a 0.5 mum 3-metal, 2-poly, n-well process and shared the same pixel and readout architectures. Two pixel structures were fabricated-the standard three transistor active pixel sensor, where the output depends on the photodiode capacitance, and one incorporating an in-pixel capacitive transimpedance amplifier where the output is dependent only on a designed feedback capacitor. The n-well /p-sub diode performed best in terms of sensitivity (an improvement of 3.5 times and 1.6 times over the n ⁺ /p-sub and p ⁺ /n-well/p-sub diodes, respectively) and signal-to-noise ratio (1. 5times and 1.2 times improvement over the n ⁺ /p-sub and p ⁺ /n-well/p-sub diodes, respectively) while the p ⁺ /n-well/p-sub diode had the minimum (33% compared to other two structures) dark current for a given sensitivity.

Traditionally, charge-coupled device (CCD)-based image sensors have held sway over the field of biomedical imaging. Complementary metal-oxide semiconductor (CMOS)-based imagers so far lack sensitivity leading to poor low-light imaging. Certain applications including our work on animal-mountable systems for imaging in awake and unrestrained rodents require the high sensitivity and image quality of CCDs and the low power consumption, flexibility, and compactness of CMOS imagers. We present a 132 × 124 high sensitivity imager array with a 20.1-μ m pixel pitch fabricated in a standard 0.5- μm CMOS process. The chip incorporates n-well/p-sub photodiodes, capacitive transimpedance amplifier (CTIA)-based in-pixel amplification, pixel scanners, and delta differencing circuits. The 5-transistor all-nMOS pixel interfaces with peripheral pMOS transistors for column-parallel CTIA. At 70 frames/s, the array has a minimum detectable signal of 4 nW/cm ² at a wavelength of 450 nm while consuming 718 μA from a 3.3-V supply. The peak signal-to-noise ratio (SNR) was 44 dB at an incident intensity of 1 μW/cm ² . Implementing 4 × 4 binning allowed the frame rate to be increased to 675 frames/s. Alternately, sensitivity could be increased to detect about 0.8 nW/cm ² while maintaining 70 frames/s. The chip was used to image single-cell fluorescence at 28 frames/s with an average SNR of 32 dB. For comparison, a cooled CCD camera imaged the same cell at 20 frames/s with an average SNR of 33.2 dB under the same illumination while consuming more than a watt.

We present a neuromorphic silicon chip that emulates the activity of the biological spinal central pattern generator (CPG) and creates locomotor patterns to support walking. The chip implements ten integrate-and-fire silicon neurons and 190 programmable digital-to-analog converters that act as synapses. This architecture allows for each neuron to make synaptic connections to any of the other neurons as well as to any of eight external input signals and one tonic bias input. The chip's functionality is confirmed by a series of experiments in which it controls the motor output of a paralyzed animal in real-time and enables it to walk along a three-meter platform. The walking is controlled under closed-loop conditions with the aide of sensory feedback that is recorded from the animal's legs and fed into the silicon CPG. Although we and others have previously described biomimetic silicon locomotor control systems for robots, this is the first demonstration of a neuromorphic device that can replace some functions of the central nervous system in vivo .