Neural net to neuronal network memristor interconnects

Ella Gale, Attya Iqbal, Jeffrey Davey, Deborah Gater

Research output: Contribution to journalArticle (Academic Journal)peer-review


Hardware-based Artificial Intelligence (A.I.) has many potential applications in biomedical technology; for example, connecting expert systems to biosensors for real-time physiological monitoring of a range of biomarkers, or interfacing with the brain.We suggest that memristors are well-placed to interface directly with neurons to interconnect between computer hardware and the brain for 3 reasons: memristors are widely-touted as neuromorphic computing elements; memristor theory has been successfully used to model spike-time dependent plasticity and the Hodgkin-Huxley model of the neuron; and, the d.c. response of the memristor is a current spike. In this chapter we show that connecting a spiking memristor network to electrically active neuronal cells causes a change in the memristor network dynamics by: removing the memristor spikes, which we show is due to the effects of connection to aqueous medium; causing a change in current decay rate consistent with a change in memristor state; presenting more-linear I −t dynamics; and increasing the memristor spiking rate, as a consequence of interaction with the active cells. This demonstrates that such cells are capable of communicating directly with memristors, without the need for computer translation.

Original languageEnglish
Pages (from-to)153-168
Number of pages16
JournalStudies in Computational Intelligence
Publication statusPublished - 2015


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