One of the defining features of the connections between neurons is that they become stronger when neurons fire together; hence the phrase “neurons that fire together, wire together”, a phenomenon otherwise known as Hebbian learning. Various experiments have shown that this effect is most pronounced early in the learning process, when the increase in connection strength is greatest. Later learning merely reinforces the links
Using a single memristor to connect two neurons, the memristance decreases when a voltage is applied which increases the current which in turn causes the memristance to drop further, in a kind of positive feedback effect. Using two memristors in series solves the problem according to work by Farnood Merrikh-Bayat and Saeed Bagheri Shouraki. Choosing their memristance carefully allows them to reproduce Hebbian-type synapse strengthening more or less exactly.
A lower memristance allows more current to flow so this certainly increases the strength of the connection as expected but there’s a problem. The positive feedback effect means that later signals have a bigger effect on the connection than earlier ones, which is the opposite way round to the way real neurons connect, where earlier signals have the strongest effect.
Physical realization of the first memristor by researchers at Hewlett Packard (HP) labs attracts so much interest in this newly found circuit element which has so many applications specially in a field of neuromorphic systems. Now, it is well known that one of the main applications of memristor is for the hardware implementation of synapses because of their capability in dense fabrication and acting as a perfect analog memory. However, synapses in biological systems have this property that by progressing in the learning process, variation rate of the synapses weights should decrease which is not the case in the currently suggested memristor-based structures of neuromorphic systems. In this paper, we show that using two dissimilar memristors connected in series as a synapse perform better than the single memristor.