We successfully constructed phase-change quantum dots string (PCQDS) systems and studied their signal responses. The PCQDS actually is a cascaded structure consisted of several stochastic resonance (SR) two-state systems, in which inherent non-linearity, i.e., phase-change of quantum dots (QDs), plays elementary and important roles to modulate signal output. We established an SR model to simulate signal responses depending on stimulation history. We know that some QDs will oscillate with input forcing frequency, while certain QDs will oscillate in their own frequency triggered by phase transition. These two effects cooperate to generate polymorphic response patterns, including action potential patterns exhibited by envelope of spike peak values. An interesting and important simulation is that we replicate the memory effect in Nb-doped AlNO, i.e., a QDs dispersed system. The result indicates that memory can occur in a system only constructed by volatile elementary units, implying memory existing in network. Long-term plasticity and spike-rate dependent plasticity can also be realized by using frequency and phase modulation. Our study provides a new scope to study signal handling and memory effect in quantum system.

All memristor neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence. It is crucial to find suitable elementary units for both performing featured neuromorphic functions and fabrication in large scale. Here a simple memristive structure, Nb/HfO_x/Pd, is proposed for this goal. Its two resistive switching mechanisms, Mott transition of NbO₂ and oxygen vacancy (V_o) migration, can be controlled by modulating external bias directions. Negative bias activates reversible phase transition and restrains V_o filament formation to allow the memristor to mimic the firing action potential. Positive bias activates V_o filament formation and restrains the other to allow the memristor to mimic synaptic plasticity and learning protocols. The system can respond adaptively to naturally generated action potentials and modified synaptic signals from the same memristive structure. In addition, some special features related to signal encoding and recognition are discovered when the system is settled according to chaos circuit theory. Our study provides a novel approach for designing elementary units for neuromorphic computations.