Abstract
A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wider signal bandwidth than that of an image sensor, a data compression technique is essentially required for arrayed neural recording systems. In this paper, compressed sensing (CS) is employed for data reduction, and a novel time-domain analog CS encoder is proposed. A simpler and lower power circuit than conventional analog or digital CS encoders can be realized by using the proposed CS encoder. A prototype of the proposed encoder was fabricated in a 180 nm 1P6M CMOS process, and it achieved an active area of 0.0342 and an energy efficiency of 25.0
Highlights
Investigating the network of the brain is the fundamental mission of neuroscience, and neural probes play an important role in this task [1]
Each input signal of the channel is represented as a pseudo-differential signal, which is converted by voltage-to-delay-time converters (VTCs) j+ and VTC j− (1 ≤ j ≤ 20), and the control voltages of VTC j+ and VTC j−
A smaller Nunit relaxes jitter requirement for VTC and to-digital converter (TDC), and low-power implementation could be realized, while higher compression ratio (CR) cannot be achieved because realizable maximum CR is same with Nunit
Summary
Investigating the network of the brain is the fundamental mission of neuroscience, and neural probes play an important role in this task [1]. By applying MEMS technology for the fabrication of neural probes [2], neural probe arrays, which have multiple electrodes on a single probe [3,4], can be fabricated. With miniaturized neural probes, integrated neural recording microsystems with CMOS LSI have been realized [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. The number of channels on a neural probe array becomes doubled in 7.4 years, which is similar to Moore’s law [24]. A greater number of recording channels and higher density of front-end circuitry is required for exponentially increasing the number of recording channels.
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