Abstract
In this paper, a feed-forward memristor-based spiking neural network is taught to separate correlated and uncorrelated synapse and learn character inputs using spike-timing-dependent plasticity (STDP). A TiO 2 non-linear drift memristor model is used to simulate a neuromorphic circuit with 25 pre- and 1 post-synaptic neuron. During the learning process, memristors are radiated with state-altering radiation and the effect on circuit learning behavior is determined. It is observed that the network recovers when radiation ceases but takes longer to resolve the correlation. Further, at lower but continuous radiation exposure, the circuit may resolve the pattern indefinitely.
Original language | American English |
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State | Published - 12 Apr 2019 |