The spike‐timing‐dependent plasticity (STDP) characteristic of the memristor plays an important role in the development of neuromorphic network computing in the future. The STDP characteristics were observed in different memristors based on different kinds of materials. The investigation regarding the influences of device hysteresis characteristic, the initial conductance of the memristors, and the waveform of the voltage pulses applied to the memristor as preneuron voltage spike and postneuron voltage spike on the STDP behavior of memristors are reviewed.
- Spike-timing-dependent plasticity
The state‐of‐the‐art artificial intelligence based on traditional von Neumann computation paradigm has shown remarkable learning and thinking abilities, for instance, AlphaGo created by the Google‐owned company Deep Mind beat the top Go player Lee Sedol by 4:1 recently . However, the information processing through the digital von Neumann computation paradigm is much less efficient as compared to human brains, which is the major bottleneck of von Neumann computation paradigm. Synapse plays the key role in learning, thinking, and memorizing for a human being, and there are approximately 1014 synapses in a human’s brain . A synapse is formed between two neuron cells , and the synapse weight can be precisely tuned by the ionic flowing through them. It is well known that the adaptation of the synapse weight between two neurons it connects with makes the biological systems functional . In order to build up a system that behaves in a much more efficient way like a human brain, people have never stopped searching for an electrical element that mimics the basic function of a synapse until “the miss memristor found .”
Similar to a biological synapse, memristor is a two‐terminal device whose conductance can be changed by the input pulses or by controlling the charge through it [4, 6] and in such a way, a memristor works as an artificial electronic synapse. Electronic synapses based on memristor devices are around three orders of magnitude smaller than a prominent CMOS design ; thus, the memristor has a great potential for scalability as compared to the electronic synapse made by traditional complex circuits .
Synapses have different kinds of plasticity, which have been realized and investigated in different memristors . And the research on the application of memristors with the common synaptic plasticity in some kind of neural networks has also been conducted. For instance, HfO2‐based memristors were used in a Hopfield neural network to implement associative memory . The relationship between the resistance of the memristor and the synaptic weight was defined. And the resistances of the memristors were tuned to the target resistances through the application of the voltage pulses on the memristors as the training process . Prezioso et al. realized pattern classification by using the neural network based on memristors with synaptic plasticity . The 12 × 12 crossbar with Pt/Al2O3/TiO2−
2. STDP in memristors
In the common synaptic plasticity mentioned above, the change of the conductance (weight) is only related to one voltage pulse applied on the memristors. Another kind of plasticity of the synapses is spike‐timing‐dependent plasticity (STDP). STDP is one of the most important synaptic characteristics. STDP modulates synapse weight based on the activities of the so‐called pre‐ and postsynaptic neurons . The spikes from both preneuron and postneuron arrive at the synapse occasionally in the opposite direction . In STDP, the change of the synaptic weight is the function of relative neuron spike timing ∆
STDP have been intensively investigated in the different memristors with different materials. The memristors are usually composed of two electrodes and memristive materials sandwiched between two electrodes. Metals such as Au, Pt, Ag, Cu, conductive nitrides such as TiN, and conductive oxides such as ITO are usually used as the materials of electrodes. The memristive materials can be grouped into binary oxides, ternary and more complex oxides, polymer, and other kind of materials.
The STDP of binary memristive materials such as TiO
Wang et al. reported that STDP was observed in the memristors based on amorphous InGaZnO. As shown in Figure 7(c, d), a pair of voltage spikes with amplitude of V+/V− = 5 V/−5 V was applied on the two terminals of the memristors with relative timing Δ
The STDP behavior was also observed in polymer such as poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) , EV(ClO4)2/BTPA‐F , and so on. Li et al. imitated the STDP of Ag/PEDOT:PSS/Ta structure . A pair of temporally correlated voltage pulses with amplitudes V+/V− = 2 V/−2 V was used as presynaptic spikes and postsynaptic spikes, which was applied to the memristors, respectively. The change of the synaptic weights related to the precise timing between pre‐ and postsynaptic spikes is shown in Figure 8(c).
Some factors in the STDP measurements can change some characteristics of the STDP, for example, the waveform of voltage spikes used to imitate the presynaptic neuron spike and postsynaptic neuron spike influences the STDP behavior significantly. It has been reported that the STDP function can be strongly influenced by the shape of the input voltage spikes . The shape of voltage spike generated from presynaptic neuron is the same with that generated from postsynaptic neuron. Zamarreño‐Ramos et al. investigated the influence of the shape of the voltage spikes (spk(
Cederström et al. investigated the role of device hysteresis characteristic of the memristors played in the operation of its STDP function. Hysteresis characteristics of memristors based on BiFeO3, Ag/Si, TiO2, and chalcogenide (PCM) were compared. STDP characteristics were simulated with different models of different memristors, and the results are shown in Figure 10. The influence of switching characteristics on the operating region used for STDP was discussed. A smooth switching characteristics leads to a much wider operation region, and a steep switching characteristics leads to a much narrower operation region .
Du et al. reported that the learning time constant can be adjusted through changing the duration of the voltage spikes. The scheme of the voltage spikes is shown in Figure 11, and pulse width (
Xiao et al. reported the STDP characteristics of the memristor with the structure of Au/polycrystal CH3NH3PbI3/ITO/PEDOT:PSS. Different waveforms were used as presynaptic neuron voltage spike and postsynaptic neuron voltage spike, which are shown in Figure 13(b–e). Four different kinds of STDP characteristics, including asymmetric Hebbian rule, asymmetric anti‐Hebbian rule, symmetric Hebbian rule, and symmetric anti‐Hebbian rule, were obtained corresponding to four different waveforms applied to the memristor as shown in Figure 13(f–i). And the four kinds of STDP behaviors were fit by different equations .
Prezioso et al. investigated the STDP characteristics of the memristor with the structure of Pt/Al2O3/TiO2−
In summary, the STDP characteristics have been observed in different memristors based on different kinds of materials, which make memristors become promising in the bio‐inspired neuromorphic application. Great efforts have also been made in the investigation on the influence factors of the STDP characteristics such as device hysteresis characteristic and the waveform of the voltage pulses applied to the memristor as preneuron voltage spike and postneuron voltage spike. Different kinds of waveform were used, and different kinds of STDP characteristics were observed.
This work was supported by the National Natural Science Foundation of China (No. 51402044, 51602039 and U1435208).
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