Neuroscience research into how complex brain functions are implemented at an extra-cellular level requires in vivo neural recording interfaces, including microelectrodes and read-out circuitry, with increased observability and spatial resolution. The trend in neural recording interfaces toward employing high-channel-count probes or 2D microelectrodes arrays with densely spaced recording sites for recording large neuronal populations makes it harder to save on resources. The low-noise, low-power requirement specifications of the analog front-end usually requires large silicon occupation, making the problem even more challenging. One common approach to alleviating this consumption area burden relies on time-division multiplexing techniques in which read-out electronics are shared, either partially or totally, between channels while preserving the spatial and temporal resolution of the recordings. In this approach, shared elements have to operate over a shorter time slot per channel and active area is thus traded off against larger operating frequencies and signal bandwidths. As a result, power consumption is only mildly affected, although other performance metrics such as in-band noise or crosstalk may be degraded, particularly if the whole read-out circuit is multiplexed at the analog front-end input. In this article, we review the different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and suggest strategies for improving performance.

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This paper describes a low-noise, low-power and high dynamic range analog front-end intended for sensing neural signals. In order to reduce interface area, a 32-channel multiplexer is implemented on circuit input. Furthermore, a spatial delta encoding is proposed to compress the signal range. A differential artifact compression algorithm is implemented to avoid saturation in the signal path, thus enabling reconstruct or suppressing artifacts in digital domain. The proposed design has been implemented using 0.18 μm TSMC technology. Experimental results shows a power consumption per channel of 1.0 μW, an input referred noise of 1.1 μVrms regarding the bandwidth of interest and a dynamic range of 91 dB.