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Review
. 2023 Mar 2;26(4):106315.
doi: 10.1016/j.isci.2023.106315. eCollection 2023 Apr 21.

Short-term synaptic plasticity in emerging devices for neuromorphic computing

Chao Li  1   2   3 , Xumeng Zhang  1   4   5 , Pei Chen  1 , Keji Zhou  1   4   5 , Jie Yu  1   4 , Guangjian Wu  1   4   5 , Du Xiang  1   4   5 , Hao Jiang  1   4   5 , Ming Wang  1   4   5 , Qi Liu  1   4   5
Affiliations
Review

Short-term synaptic plasticity in emerging devices for neuromorphic computing

Chao Li et al. iScience. .

Abstract

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

Keywords: Applied computing; Devices; Engineering.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Functions of synaptic plasticity Top panel: schematic diagram of the STP correspondence between biological and emerging artificial synapses, from the basic mechanisms to system implementation. Bottom panel: the behavioral and functional differences between LTP and STP in biological synapses.
Figure 2
Figure 2
Biological synapse and STP behaviors (A) Schematic of a biological synapse: spike input from presynaptic neuron causes the injection of Ca2+, resulting in the release of vesicles. These vesicles are rapidly replenished by a readily releasable vesicle pool. Most vesicles are stored in a large reserve pool and can be utilized to refill the releasable pool. Released transmitters are recovered by endocytic process into releasable and reserve pool. (B) STP behaviors, including PPF, PPD, and PTP. PPF is proposed to originate from the accumulation of calcium ions in response to multiple presynaptic stimuli, leading to increased vesicle release. PPD is associated with the depletion of vesicles, since the releasable pool cannot recover in time to cause a decrease in neurotransmitter release under high-frequency stimulation. PTP might be influenced by the activation of protein kinases, which promotes vesicle-membrane fusion and leads to longer timescale in response to tonic stimuli. (C) Schematic diagram of STP response. PPF and PTP could strengthen the synaptic weights, while PPD inhibits the synaptic weights. PTP exhibits experience-dependent behavior in comparison to PPF.
Figure 3
Figure 3
Physics for emerging short-term artificial synaptic devices Short-term mechanisms are presented in six panels. The metastable state indicates the transition in the device under the applied stimulus. The dash lines represent the physical processes of returning to the stable states upon removal of the stimuli. The timescale refers to the duration of the metastable state. The dynamic response characterizes the metric change in the device under pulsed stimulation. For ionic- and electronic-based devices, the response can be expressed by the change of the current under pulses. The position of skyrmions and polarization represent the dynamic response of the magnetic- and ferroelectric-based devices respectively.
Figure 4
Figure 4
STP implementation in emerging devices (A) Comparison of ion dynamics in the diffusive SiOxNy:Ag memristor with that in biological synapses. Left panel: Diffusion of Ca2+ from extracellular sources via VGCCs and receptors, and the removal of Ca2+ via exchanger. Right panel: Ag diffuses into the dielectric layer to form filaments under the electrical field and removes by interfacial energy or mechanical stress. Reproduced with permission from ref. , Springer Nature. (B) Schematic of the switching process inside the ion migration device. Filaments are formed through ions migration under the electric field and relax to clusters after stimuli. (C) Experimental demonstration of PPD following PPF in the diffusive SiOxNy:Ag memristor. Device current (blue) responses to a voltage pulse train with the same amplitudes but different frequencies. Reproduced with permission from ref. , Springer Nature.
Figure 5
Figure 5
Temporal filtering in biology and physical devices (A) Left panel: examples of EPSCs recorded in response to an irregular stimulus train with an average rate of 20 Hz at the climbing fiber (CF), parallel fiber (PF) and Schaffer collateral (SC) synapses. Right panel: steady-state EPSC as a function of stimulus rate. Reproduced with permission from ref. , Springer Nature. (B) Indium-zinc-oxide (IZO)-based protonic/electronic hybrid transistor. Protons are laterally coupled under electric fields to produce a change in conductance. Reproduced with permission from ref. , Springer Nature. (C) Frequency responses of the IZO-based EGT. The lateral coupling of protons increase with sequence inputs, and high-pass filtering is realized by the PPF effect. Reproduced with permission from ref. , Springer Nature. (D) Facilitatation and depression are realized respectively in single EGT by linking the gate to input and ground. When the gate is positively stimulated, channel can induce charges. In contrast, when the gate is grounded, charges in the channel would be excluded. Reproduced with permission from ref. , Wiley. (E) The analog circuit containing two EGTs connected in high- and low-pass modes is able to achieve a band-pass filter. Reproduced with permission from ref. , Wiley.
Figure 6
Figure 6
Sound localization in biology and physical devices (A) Schematic image of sound location by ITD and ILD effects in human. Reproduced with permission from ref. , the American Physiological Society. (B) Sample binaural signals at high and low frequencies. ITD is embodied in the phase differences and ILD in the amplitudes. Reproduced with permission from ref. , Springer Nature. (C) Amplification of the ITD at the acoustic, mechanical, and neuronal levels. Reproduced with permission from ref. , Elsevier. (D) A schematic structure of the neuro-transistor with multiple in-plane gates in one direction, which are regarded as presynaptic terminals on a single dendritic branch of a neuron. Reproduced with permission from ref. , Wiley. (E) The ratio of the current amplitude as a function of the time interval and the sound azimuth. Reproduced with permission from ref. , Wiley. (F) A sound localization system with amplifying the ITD effect. Reproduced with permission from ref. , American Chemical Society.
Figure 7
Figure 7
Reservoir computing in physical devices (A) Schematic of a conventional RC system. Emerging devices act as virtual nodes in the RC network. Reproduced with permission from ref. , Springer Nature. (B) Basic dynamic response for RC system. Nonlinearity maps information to a high-dimensional space for easy classification. Short-term memory enables the encoding of temporal information. Reproduced from ref. , CC BY license. (C) Electrical synapses are able to produce history-dependent responses under different encoded sequence inputs in every frame. Reproduced from ref. , CC BY license. (D) Photo-synapses are able to sense optical stimulus and update conductance according to the optical sequences. Reproduced from ref. , CC BY license. (E) Processing of inputs and sampling of outputs. Sample1 (SMP1) and sample2 (SMP2) are measured as classification basis. Reproduced from ref. , CC BY license. (F) Handwritten digit recognition using a memristor-based RC system. The pixels are encoded as spike sequences with different frequencies as nodes’ inputs. Reproduced from ref. , CC BY license. (G) Autonomous forecasting of Mackey-Glass time series using a memristor-based RC system. Reproduced with permission from ref. , Springer Nature.
Figure 8
Figure 8
Roadmap with past milestones and future prospects in artificial synapses Device-level LTP reproduced with permission from ref., American Chemical Society. Device-level STP reproduced with permission from ref., Springer Nature. Array-level LTP reproduced with permission from ref., Springer Nature. Mixed-timescale plasticity reproduced with permission from ref., Springer Nature.
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