Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor

Abstract

"Memory" is an essential building block in learning and decision-making in biological systems. Unlike modern semiconductor memory devices, needless to say, human memory is by no means eternal. Yet, forgetfulness is not always a disadvantage since it releases memory storage for more important or more frequently accessed pieces of information and is thought to be necessary for individuals to adapt to new environments. Eventually, only memories that are of significance are transformed from short-term memory into long-term memory through repeated stimulation. In this study, we show experimentally that the retention loss in a nanoscale memristor device bears striking resemblance to memory loss in biological systems. By stimulating the memristor with repeated voltage pulses, we observe an effect analogous to memory transition in biological systems with much improved retention time accompanied by additional structural changes in the memristor. We verify that not only the shape or the total number of stimuli is influential, but also the time interval between stimulation pulses (i.e., the stimulation rate) plays a crucial role in determining the effectiveness of the transition. The memory enhancement and transition of the memristor device was explained from the microscopic picture of impurity redistribution and can be qualitatively described by the same equations governing biological memories.

Artificial synapse network on inorganic proton conductor for neuromorphic systems

“…Such EPSC behaviour is quite similar to an EPSC process in a biological excitatory synapse 34 . The energy dissipation of single spike event is estimated to be B45 pJ, which is lower than that of the artificial synapse based on conventional CMOS circuit 35 and is comparable to the energy dissipation of the reported hardware-based artificial synapse 8,9,15 . At present, the channel width of our synaptic transistors is large (B1 mm), and it can be scaled down to submicrometer scale by a photolithography method.…”

“…Correspondingly, the measured current in the self-assembled IZO channel will decrease gradually back to the resting current. Such proton migration-induced EPSC behaviour can be explained with the stretched exponential model and the decay of EPSC could be fitted by a stretched exponential function (SEF) 8,36 :…”

“…In the beginning, synaptic functions were emulated by complementary metal oxide semiconductor (CMOS) neuromorphic circuits, but such CMOS circuits consumed substantially more energy than a biological synapse, and it is hard to scale up the circuits to a size comparable with the brain 5 . Recently, resistive switching memory, memristors or atomic switch has been investigated in biologically inspired neuromorphic circuits [6][7][8][9][10][11] . Important synaptic learning rules such as spike-timing-dependent plasticity (STDP) and short-term memory to long-term memory transition have been demonstrated.…”

Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing

“…This process is similar to the trend of synaptic weight variation of the human-memory "forgetting curve" in psychology. [43,44] In this work, we proceed the gradually set operation with different current compliance 0.5, 1, 2, and 3 mA, then the smaller voltage 0.01 V was employed to read the current of cell. A decay "forgetting curves" were also observed.…”

Phenomenological modeling of memristive devices

“…The advantage of such equivalent circuit is that it naturally bounds the device resistance and provides physically plausible switching behavior of the device near extreme on and off states. Additionally, the equivalent circuit can be extended to include secondary volatile switching behavior [5,10,30,33,34] and electronic noise [14]. Finally, a practical way to include switching variations is to modify parameter f in the model, for example, by adding a zero-mean Gaussian random variable to mimic device-to-device and cycle-to-cycle variations.…”