Short-term Plasticity Research Articles

Extracellular matrix integrity regulates GABAergic plasticity in the hippocampus

The brain's extracellular matrix (ECM) is crucial for neural circuit functionality, synaptic plasticity, and learning. While the role of the ECM in excitatory synapses has been extensively studied, its influence on inhibitory synapses, particularly on GABAergic long-term plasticity, remains poorly understood. This study aims to elucidate the effects of ECM components on inhibitory synaptic transmission and plasticity in the hippocampal CA1 region. We focus on the roles of chondroitin sulfate proteoglycans (CSPGs) and hyaluronic acid in modulating inhibitory postsynaptic currents (IPSCs) at two distinct inhibitory synapses formed by somatostatin (SST)-positive and parvalbumin (PV)-positive interneurons onto pyramidal cells (PCs). Using optogenetic stimulation in brain slices, we observed that acute degradation of ECM constituents by hyaluronidase or chondroitinase-ABC did not affect basal inhibitory synaptic transmission. However, short-term plasticity, particularly burst-induced depression, was enhanced at PV→PC synapses following enzymatic treatments. Long-term plasticity experiments demonstrated that CSPGs are essential for NMDA-induced iLTP at SST→PC synapses, whereas the digestion of hyaluronic acid by hyaluronidase impaired iLTP at PV→PC synapses. This indicates a synapse-specific role of CSPGs and hyaluronic acid in regulating GABAergic plasticity. Additionally, we report the presence of cryptic GABAergic plasticity at PV→PC synapses induced by prolonged NMDA application, which became evident after CSPG digestion and was absent under control conditions. Our results underscore the differential impact of ECM degradation on inhibitory synaptic plasticity, highlighting the synapse-specific interplay between ECM components and specific GABAergic synapses. This offers new perspectives in studies on learning and critical period timing.

Artificial photoelectric synaptic devices with ferroelectric diode effect for high-performance neuromorphic computing

With the rapid advancement of artificial intelligence and machine learning, the demand for neuromorphic computing systems has intensified. High-performance artificial synaptic devices are crucial to achieving this objective. Ferroelectric diode artificial synaptic devices were prepared using aluminum-doped BaTiO3 (BTAO) thin films by a low-cost sol-gel method. These devices mimic the basic properties of biological synapses, such as long-term plasticity (LTP) and short-term plasticity (STP), under electrical stimulation. Additionally, the devices exhibit an excellent UV light response, enabling the transition from STP to LTP by adjusting the light pulse intensity, width, and number of pulses. Aluminum doping significantly enhances the ferroelectricity of BaTiO3 films, increasing the Pr from 2.31 µC/cm² to 9.08 µC/cm². By modulating the Schottky barrier through polarization, the BTAO films exhibit a switchable diode effect, which facilitates fine-tuning of the synaptic connection strength while maintaining synaptic stability. Recognition accuracies of 97.32% for the MNIST dataset and 87.48% for the Fashion-MNIST dataset were achieved by convolutional neural network simulations. These results suggest new possibilities for brain-like processing with ferroelectric diodes.

Activation of hypoactive parvalbumin-positive fast-spiking interneurons restores dentate inhibition to reduce electrographic seizures in the mouse intrahippocampal kainate model of temporal lobe epilepsy

Parvalbumin-positive (PV+) GABAergic interneurons in the dentate gyrus provide powerful perisomatic inhibition of dentate granule cells (DGCs) to prevent overexcitation and maintain the stability of dentate gyrus circuits. Most dentate PV+ interneurons survive status epilepticus, but surviving PV+ interneuron mediated inhibition is compromised in the dentate gyrus shortly after status epilepticus, contributing to epileptogenesis in temporal lobe epilepsy. It is uncertain whether the impaired activity of dentate PV+ interneurons recovers at later times or if it continues for months following status epilepticus. The development of compensatory modifications related to PV+ interneuron circuits in the months following status epilepticus is unknown, although reduced dentate GABAergic inhibition persists long after status epilepticus. We employed whole-cell patch-clamp recordings from dentate PV+ interneurons and DGCs in slices from male and female sham controls and intrahippocampal kainate (IHK) treated mice that developed spontaneous seizures months after status epilepticus to study epilepsy-associated changes in dentate PV+ interneuron circuits. Electrical recordings showed that: 1) Action potential firing rates of dentate PV+ interneurons were reduced in IHK treated mice up to four months after status epilepticus; 2) spontaneous inhibitory postsynaptic currents (sIPSCs) in DGCs exhibited reduced frequency but increased amplitude in IHK treated mice; and 3) the amplitude of IPSCs in DGCs evoked by optogenetic activation of dentate PV+ cells was upregulated without changes in short-term plasticity. Video-EEG recordings revealed that IHK treated mice showed spontaneous electrographic seizures in the dentate gyrus and that chemogenetic activation of PV+ interneurons abolished electrographic seizures. Our results suggest not only that the compensatory changes in PV+ interneuron circuits develop after IHK treatment, but also that increased PV+ interneuron mediated inhibition in the dentate gyrus may compensate for cell loss and reduced intrinsic excitability of dentate PV+ interneurons to stop seizures in temporal lobe epilepsy.

Gradient Mo1−xWxSe2 monolayer Alloys: Synthesis and multifunctional applications

With the continuous and rapid development of electronic information technology, the demand for new materials with miniaturization, high integration, high performance, and multifunction is becoming increasingly urgent. However, it is difficult for existing photodetectors based on a single material to achieve multi-functionality while maintaining excellent performance, and the devices based on heterostructures are faced with complex preparation and compatibility problems. In this work, gradient Mo1−xWxSe2 monolayer alloys with high crystal quality have been synthesized by an evaporation-assisted chemical vapor deposition method. The composition of the synthesized Mo1−xWxSe2 monolayer alloys varies gradually with x ranging from 0 to 1. The Mo1−xWxSe2 based photodetector shows superior photoresponse performance in the visible wavelength with maximum responsivity of 26 A/W and external quantum efficiency up to 6320 % under 520 nm laser irradiation. Besides, the Mo1−xWxSe2 device is capable of high-resolution imaging, which can be used as image sensor. Moreover, nonvolatile memory function has also been realized in the device because the band gap of Mo1−xWxSe2 can be effectively adjusted by the gradient distribution of the component and the deep energy levels caused by Se vacancies can be modulated to shallow energy levels. A thrilling discovery is that the gradient Mo1−xWxSe2 alloy device can simulate the biological synapses successfully, including paired-pulse facilitation, short-term plasticity, long-term plasticity and learning process. The realization of high-performance photodetection, visualization, nonvolatile memory and synaptic simulation based on a single gradient crystal offers a new type of material to develop advanced optoelectronic devices with multiple functions.

Xanthan gum biopolymer gated low voltage-operating photo synaptic organic field-effect transistor for neuromorphic electronics

Low voltage-operating light-stimulated organic field effect transistors (OFETs) are attracting huge attention due to their potential for developing photonic neuromorphic synapse-based neural networks resembling the human brain. The OFETs based on environment friendly biocompatible materials and processability are highly desired for realizing photo synaptic elements. In this report, we demonstrate a low voltage operating xanthan gum-gated poly(2,5-bis(3-alkylthiophen-2-yl) thieno[3,2-b] thiophene) [PBTTT-C14] based OFETs, exhibiting photo synaptic behavior at −0.3V. The basic photo response parameters such as photoresponsivity, detectivity, and photocurrent to dark current ratio of the OFETs are estimated to quantify the photo synaptic behavior of the OFETs. We report the fundamental neurobiological characteristics such as excitatory post-synaptic current (EPSC), pair pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), learning-forgetting-memorizing (LFM), and conversion of STP to LTP for the xanthan gum-gated PBTTT-C14-based OFETs. Furthermore, we showcase the electronic "OR" logic operations by employing the OFETs and replicate the Pavlovian conditioning experiment to mimic the classical learning phenomenon. Using these photo synaptic OFETs, we replicate human emotion and mood-swing-dependence learning and memory behavior which is an important aspect of cognitive learning. The OFETs exhibit fairly low energy consumption ~100 pJ per optical operation to perform synaptic responses. We interpret and attribute the observed neuromorphic behavior to the charge-trapping occurring at the semiconductor-dielectric interface and bulk traps in OFETs. The presented phenomena broaden opportunities to the device communities for building an artificial complex neural network using energy-efficient bio-polymer gated photo synaptic OFETs.

Colloidal Quantum Dots‐Based Photoelectrochemical‐Type Optoelectronic Synapse

AbstractCurrent artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Full van der Waals Ambipolar Ferroelectric Configurable Optical Hetero-Synapses for In-Sensor Computing.

The rapid development of visual neuromorphic hardware can be attributed to their ability to capture, store and process optical signals from the environment. The main limitation of existing visual neuromorphic hardware is that the realization of complex functions is premised on the increase of manufacturing cost, hardware volume and energy consumption. In this study, we demonstrated an optical synaptic device based on a three-terminal van der Waals (vdW) heterojunction that can realize the sensing functions of light wavelength and intensity as well as short-term and long-term synaptic plasticity. In the image recognition task, we constructed an optical reservoir neural network (ORNN) and a visible light communication system (VLC) composed of this optical synaptic device. The ORNN has a recognition rate of up to 84.9% for 50 000 color images in 10 categories in the CIFAR-10 color image dataset, and the VLC system can achieve high-speed transmission with an ultra-low power consumption of only 0.4 nW. This work shows that through reasonable design, vdW heterojunction structures have great application potential in low-power multifunctional fusion application tasks such as visual bionics.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

Tailored Environment-Friendly Reverse Type-I Colloidal Quantum Dots for a Near-Infrared Optical Synapse and Artificial Vision System.

Colloidal quantum dots (QDs) are emerging as potential candidates for constructing near-infrared (NIR) photodetectors (PDs) and artificial optoelectronic synapses due to solution processability and a tunable bandgap. However, most of the current NIR QDs-optoelectronic devices are still fabricated using QDs with incorporated harmful heavy metals of lead (Pb) and mercury (Hg), showing potential health and environment risks. In this work, we tailored eco-friendly reverse type-I ZnSe/InP QDs by copper (Cu) doping and extended the photoresponse from the visible to NIR region. Transient absorption spectroscopy analysis revealed the presence of Cu dopant states in ZnSe/InP:Cu QDs that facilitated the extraction of photogenerated charge carriers, leading to an enhanced photodetection performance. Specifically, under 400 nm illumination, the Cu-doped ZnSe/InP QDs-based PDs presented a broadband photodetection ranging from ultraviolet (UV) to NIR, with a responsivity of 70.5 A W-1 and detectivity of 2.8 × 1011 Jones, surpassing those of the undoped ZnSe/InP QDs-based PDs (49.4 A W-1 and 1.9 × 1011 Jones, respectively). More importantly, the ZnSe/InP:Cu QDs-PDs demonstrated various synapse-like characteristics of short-term plasticity (STP), long-term plasticity (LTP), and learning-forging-relearning under NIR light illumination, which were further used to construct PD array devices for simulating the artificial visual system that is available in prospective optical neuromorphic applications.

Ultra-Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe2 for Neuromorphic Computing.

Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe2) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.

Fabrication of carbon nanotube neuromorphic thin film transistor arrays and their applications for flexible olfactory-visual multisensory synergy recognition

Abstract Artificial multisensory devices play a key role for the human-computer interaction in the field of artificial intelligence (AI). In this work, we have designed and constructed a novel olfactory-visual bimodal neuromorphic carbon nanotube thin film transistor (TFT) arrays for artificial olfactory-visual multisensory synergy recognition with the ultralow single-pulse power consumption of 25 aJ, employing semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials and gas sensitive materials, and poly[[4,8-bis[5-(2-ethylhexyl)-2-thienyl]benzo[1,2-b:4,5-b0]dithiophene-2,6-diyl]-2,5-thiophenediyl-[5,7-bis(2-ethylhexyl)-4,8-dioxo-4H,8H-benzo[1,2-c:4,5-c0]dithio-phene-1,3-diyl]] (PBDB-T) as the photosensitive material. It is noted that it is the first time to realize the simulation of olfactory and visual senses (from 280 nm to 650 nm) with the wide operating temperature range (0-150 °C) in a single SWCNT TFT device and successfully simulate the recovery of olfactory senses after COVID-19 by olfactory-visual synergy. Furthermore, our SWCNT neuromorphic TFT devices with high Ion/Ioff ratio (up to 106) at a low operating voltage (-2-0.5 V) can mimic not only the basic biological synaptic functions of olfaction and vision (such as paired-pulse facilitation, short-term plasticity, and long-term plasticity), but also optical wireless communication by Morse code. The proposed multisensory, broadband light-responsive, low-power synaptic devices provide great potential for developing AI robots to face complex external environments.

A multilevel resistive switching memristor based on flexible organic–inorganic hybrid film with recognition function

Abstract Brain-inspired neuromorphic computing systems fueled the emergence of memristor-based artificial synapses, however, conventional silicon-based devices restricted their usage in the wearable field because of their difficulty in bending. To tackle the above challenge, a vertically structured flexible memristor with aluminum-based hydroquinone organic–inorganic hybrid film and Al2O3 as the functional layer, ITO and Pt as the bottom and top electrodes, and PET as the substrate has been developed utilizing molecular/atomic layer deposition to achieve a tradeoff between the resistive transition properties and the flexibility of memristors. The obtained devices combine stable resistive switching behavior and flexibility, showing high switching ratio of 103, better retention (up to 105 s) and endurance properties (up to 104 cycles), and robustness at radius of curvature of 4.5 mm after 104 bending cycles. Furthermore, the presence of multilevel resistive states in these devices ensures that the memristor can emulate synaptic properties such as paired-pulse facilitation, transition from short-term plasticity to long-term plasticity, long-term potentiation and depression, and spike-time-dependent plasticity. The resistive switching mechanism and the role of the bending state on the electrical performance of the device are explored. The fully connected artificial neural network based on the memristor can achieve a recognition accuracy of 90.2% for handwritten digits after training and learning. Flexible memristor will bring feasible advances to the integration of neuromorphic computing and wearable functionality.

In-Situ Fabricated Transparent Flexible Nanowire Device with Wavelength-Regulated Dual-Function of Photodetector and Photonic Synapse.

Integrating the dual functionalities of a photodetector and photonic synapse into a single device is challenging due to their conflicting requirements for photocurrent decay rates. This study addresses this issue by seamlessly depositing transparent indium tin oxide (ITO) electrodes onto self-oriented copper hexadecafluoro-phthalocyanine (F16CuPc) nanowires growing horizontally along hot-stamped periodic nanogrooves on a transparent flexible polyimide plastic film. This in-situ-fabricated device achieves bending-stable dual functionalities through wavelength regulation while maintaining high transparency and flexibility. Upon exposure to 450-850 nm light, the device exhibits a rapid and sensitive photoresponse with excellent bending stability, making it ideal for optical sensing in both visible and near-infrared spectra. More importantly, the device exhibits a bending-stable excitation postsynaptic current when exposed to light spikes below 405 nm. This enables the successful emulation of various biological synaptic functionalities, including paired-pulse facilitation, spike-number-dependent plasticity, spike-duration-dependent plasticity, spike-rating-dependent plasticity, configurable plasticity between short-term plasticity and long-term plasticity, and memory learning capabilities. Utilizing this device in an artificial neural network achieves a recognition rate of 95% after 57 training epochs. Its ability to switch between photodetection and synaptic modes by adjusting the light wavelength marks a significant advancement in the field of multifunctional flexible electronics based on nanowire arrays.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation

Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.

IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing.

Nanofiber neuromorphic transistors are regarded as promising candidates for mimicking brain-like learning and advancing high-performance computing. Composite nanofibers (CNFs) typically exhibit enhanced optoelectronic and mechanical properties. In this study, indium-gallium-zinc oxide (IGZO)/polyvinylpyrrolidone (PVP) CNFs were synthesized, and the neuromorphic transistor was integrated on both rigid and flexible substrates. The learning behavior, characterized by the transition from short-term plasticity (STP) to long-term plasticity, was achieved through photoelectric stimulation of the rigid neuromorphic transistor. The nonlinear STP was simulated by the flexible neuromorphic transistor through electrical pulses, matching effectively with a reservoir computing (RC) system. Hand gesture recognition with little energy consumption (49 pJ per reservoir state) and a maximum accuracy of 92.86% has been achieved by the RC system, proving the substantial potential of the IGZO/PVP CNF neuromorphic transistor for wearable intelligent processing tasks.

Dual-Mode Reconfigurable Split-Gate Logic Transistor through Van der Waals Integration.

As silicon-based transistors approach their physical size limitations, two-dimensional material-based reconfigurable functional electronic devices are considered the most promising novel device architectures beyond Moore strategies. While these devices have garnered significant attention, they often require complex device fabrication processes and extra electric fields. Additionally, the device performance is usually limited by the metal-semiconductor interface properties. In this Letter, we have constructed a reconfigurable logic device based on a WSe2 transistor with a nanofloating gate and split-gates through van der Waals integration. This device achieves a small Schottky barrier height due to the van der Waals contacts. By varying the split-gate biases, we can realize volatile reconfigurable homojunctions as well as AND, OR, NOR, and NAND logic operations with just a single device. Furthermore, with the charge trapping effect of nanofloating gate, we can also achieve nonvolatile reconfigurable homojunctions, as well as AND and OR logic operations. The volatile and nonvolatile logic operations are similar to the short-term plasticity and long-term plasticity, respectively, of synapses in the human brain. This work offers a potential approach for creating novel reconfigurable functional electronic devices with a simple fabrication process and low cost.

Short-term plasticity and context-dependent circuit function: Insights from retinal circuitry

Changes in synaptic strength across timescales are integral to algorithmic operations of neural circuits. However, pinpointing synaptic loci that undergo plasticity in intact brain circuits and delineating contributions of synaptic plasticity to circuit function remain challenging. The whole-mount retina preparation provides an accessible platform for measuring plasticity at specific synapses while monitoring circuit-level behaviors during visual processing ex vivo. In this review, we discuss insights gained from retina studies into the versatile roles of short-term synaptic plasticity in context-dependent circuit functions. Plasticity at single synapse level greatly expands the algorithms of common microcircuit motifs and contributes to diverse circuit-level behaviors such as gain modulation, selective gating, and stimulus-dependent excitatory/inhibitory balance. Examples in retinal circuitry offer unequivocal support that synaptic plasticity increases the computational capacity of hardwired neural circuitry.

Modulation of Neurotransmission by Acid-Sensing Ion Channels.

Interstitial pH fluctuations occur normally in the brain and significantly modulate neuronal functions. Acid-sensing ion channels (ASICs), which serve as neuronal acid chemosensors, play important roles in synaptic plasticity, learning, and memory. However, the specific mechanisms by which ASICs influence neurotransmission remain elusive. Here, we report that ASICs modulate transmitter release and axonal excitability at a glutamatergic synapse in the rat and mouse hippocampus. Blocking ASIC1a channels with the tarantula peptide psalmotoxin 1 down-regulates basal transmission and alters short-term plasticity. Notably, the effect of psalmotoxin 1 on ASIC-mediated modulation is age-dependent, occurring only during a limited postnatal period (postnatal weeks 2-6). This finding suggests that protons, through the activation of ASICs, may act as modulators in synapse formation and maturation during early development.

Solution-Processed Polymer Memcapacitors with Stimulus-Controlled and Evolvable Synaptic Functionalities: From Short-Term Plasticity to Long-Term Plasticity to Metaplasticity.

In the vanguard of neuromorphic engineering, we develop a paradigm of biocompatible polymer memcapacitors using a seamless solution process, unleashing comprehensive synaptic capabilities depending on both the stimulation form and history. Like the human brain to learn and adapt, the memcapacitors exhibit analogue-type and evolvable capacitance shifts that mirror the complex flexibility of synaptic strengthening and weakening. With increasing frequency and intensity of the stimulation, the memcapacitors demonstrate an evolution from short-term plasticity (STP) to long-term plasticity (LTP), and even to metaplasticity (MP) at a higher level. A physical picture, featuring the stimulus-controlled spatiotemporal ion redistribution in the polymer, elaborates the origin of the memcapacitive prowess and resultant versatile synaptic plasticity. The distinctive MP behavior endows the memcapacitors with a dynamic learning rate (LR), which is utilized in an artificial neural network. The superiority of implementing a dynamic LR compared with conventional practices of using constant LR shines light on the potential of the memcapacitors to exploit organic neuromorphic computing hardware.