LAUSR

Abstract Adaptation of synaptic strength is central to memory and learning in biological systems, enabling important high-level processes such as the ability of animals to respond to a changing environment. Memristor devices are a promising new, nanoscale technology that emulates the function of synapses and can therefore be used to represent synaptic elements in analog artificial neural networks. The main mechanism to carry out unsupervised adaptation of weights in memristive synapses currently involves artificial spiking neural network designs relying on spike-timing dependent plasticity (STDP). We present and analyze a new memristive circuit that in addition to STDP learning rules also implements competitive adjustment based on relative timing of presynaptic inputs. The cooperative effect of multiple learning rules in the new circuit may ameliorate the problem of erasure of synaptic weights.