TY - JOUR
T1 - Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes
AU - Gupta, Isha
AU - Serb, Alexantrou
AU - Khiat, Ali
AU - Zeitler, Ralf
AU - Vassanelli, Stefano
AU - Prodromakis, Themistoklis
N1 - Funding Information:
Manuscript received August 29, 2017; revised November 30, 2017 and January 16, 2018; accepted January 21, 2018. Date of publication March 1, 2018; date of current version March 22, 2018. This work was supported by FP7 RAMP and EPSRC EP/K017829/1. Experimental procedures involving the use of animals were approved within the RAMP projects by the Ethics Committee of the University of Padova and the Italian Ministry of Health (authorisation.447/2015-PR). All the experiments were conducted in accordance with the approved guidelines. This paper was recommended by Associate Editor M. Sahin. (Corresponding author: Isha Gupta.) I. Gupta, A. Serb, A. Khiat, and T. Prodromakis are with the Southampton Nano Group, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2007-2012 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.
AB - Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.
KW - Integrating sensor
KW - memristors
KW - metastable resistive state
KW - neural recordings
KW - RRAM
KW - volatility
KW - volatility module
UR - http://www.scopus.com/inward/record.url?scp=85042845477&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2018.2797939
DO - 10.1109/TBCAS.2018.2797939
M3 - Article
C2 - 29570062
AN - SCOPUS:85042845477
SN - 1932-4545
VL - 12
SP - 351
EP - 359
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 2
ER -