Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes

Isha Gupta*, Alexantrou Serb, Ali Khiat, Ralf Zeitler, Stefano Vassanelli, Themistoklis Prodromakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Pages (from-to)351-359
Number of pages9
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number2
Early online date1 Mar 2018
Publication statusPublished - Apr 2018

Keywords / Materials (for Non-textual outputs)

  • Integrating sensor
  • memristors
  • metastable resistive state
  • neural recordings
  • RRAM
  • volatility
  • volatility module


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