In this work, a memristor-based spiking neural network with a many-to-one feed-forward topology is designed for spatio-temporal pattern learning (25-pixel character ‘B'). A TiO2 non-linear drift behavioral memristor model is used for simulation of a neuromorphic circuit with 25 pre- and 1 post-synaptic neuron. The memristor model mimics synaptic behavior and is modified to include effects of radiation interactions on memristor. Simulated irradiation of the memristors during the learning process alters their state, and the effect on circuit learning behavior is observed. The network uses spike-timing-dependent plasticity (STDP) learning implemented using biphasic neuron action potentials. It is found that although radiation changes the synaptic weights, the network is able to recover and relearn the pattern after radiation ceases. Recovery time is proportional to flux, intensity, and duration of the radiation. If the network is exposed to radiation events of low intensity and flux even for an indefinite period (as observed in space), it may be able to retain its pattern recognition capabilities.