Mimicking synaptic plasticity in memristive neuromorphic systems
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Development of artificial intelligence has been disappointing in many aspects, and has been severely limited by the basic architecture of computers. The new field of neuromorphic engineering tries to tackle this problem by basing circuit design on brain architecture. There are two features of the brain that people try to implement especially: massive parallelism and plasticity. Synapse implementations, however, have proven difficult, due to a lack of inherently plastic circuit elements. This leads to the need of overly complex circuits to mimic any kind of plasticity. Recent developments in nanotechnology provide us with an exciting new opportunity: the memristor. The memristor is basically a resistor whose resistance depends on the amount of current that passed through it: effectively it is a plastic resistor. This is the first element of its kind and could potentially revolutionize the field of neuromorphic engineering. This paper will study the viability of the memristor as a plastic synapse by reviewing the recent developments in memristive technologies separately and in combination with known theories of plasticity in the brain. Memristors turn out to be very powerful for mimicking synaptic plasticity, but current research has focused too much on spiking based learning mechanisms and not enough experimental work has been done. It also seems the memristor-based learning rules could potentially improve our understanding of the underlying neuroscience, but little work has been done on this. Finally, despite promises of memristor-based circuitry being able to match the complexity and scale of the brain, current memristors would use too much energy. Future research should focus on these three issues.