Neural recording with high-channel count microelectrode arrays: When size matters
Prof. Manuel Delgado-Restituto
Manuel Delgado-Restituto received the Ph.D. degree in Electronic Physics (Honors) from the University of Seville, Spain, in 1996. Since then, he has been working with the Institute of Microelectronics of Seville (IMSE-Univ. of Sevilla) where he currently heads a research group on low-power medical microelectronics and works in the design of silicon and optoelectronic microsystems for understanding biological neural systems, the development of neural prostheses and brain-machine interfaces, the implementation of wireless body area network transceivers and the realization of RFID transponders with biomedical sensing capabilities.
Manuel has co-authored 2 books, more than 20 chapters in contributed books, and some 200 articles in peer-review specialized publications. He also holds 7 patents.
Manuel seved as an Associate Editor for different IEEE Publications (TCAS-I, TCAS-II and TBioCAS) and as Editor-in-Chief for the IEEE JOURNAL on EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS. He also served as Vice President for Publications of the IEEE Circuits and Systems Society and now is serving as President-Elect of this Society. He has also served (or is serving) in the Organizing Committee of different international conferences, including his role as General co-Chair for ISCAS 2020 and as Technical Program co-Chair of ISCAS 2022.
Neuroscience research into how complex brain functions are implemented at a 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 towards 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 area consumption 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 talk, we review the different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and point out to strategies for improving performance.