Design considerations for neural amplifiers.

The initial amplification stage is a critical element of a neural signal acquisition system, and the design of low-noise, low-power amplifiers has received a great deal of attention in recent publications. In this paper we discuss practical considerations for the design of amplifiers intended for neural interfaces. Noise is a major issue due to the low amplitude of neural signals.

On the impact of approximate computation in an analog DeSTIN architecture.

Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale problems. The highly parallel computations required to implement large-scale deep learning systems are well suited to custom hardware.

Design of ultra-low power biopotential amplifiers for biosignal acquisition applications.

Rapid development in miniature implantable electronics are expediting advances in neuroscience by allowing observation and control of neural activities. The first stage of an implantable biosignal recording system, a low-noise biopotential amplifier (BPA), is critical to the overall power and noise performance of the system. In order to integrate a large number of front-end amplifiers in multichannel implantable systems, the power consumption of each amplifier must be minimized.

A sub-microwatt low-noise amplifier for neural recording.

In this paper we present a pre-amplifier designed for neural recording applications. Extremely low power dissipation is achieved by operating in an open-loop configuration, restricting the circuit to a single current branch, and reusing current to improve noise performance. Our amplifier exhibits 3.