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Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems.

A novel programming method for multilevel resistive random access memory (RRAM) is proposed and experimentally demonstrated to improve the stability of multilevel high resistance states (HRS). By substituting a relatively long reset pulse with a series of short pulses, this method effectively reduces the resistance variation resulted from Joule heating. The standard deviation of one HRS is greatly reduced. Though this method is demonstrated on our TiN/TaOX/Pt devices, further analysis indicates its possible validity in bipolar RRAM devices based on other transition metal oxides. Stable multilevel RRAM for applications in memory chips can be achieved through this method.