Long-term Plasticity Research Articles

PI3K couples long-term synaptic potentiation with cofilin recruitment and actin polymerization in dendritic spines via its regulatory subunit p85α

Long-term synaptic plasticity is typically associated with morphological changes in synaptic connections. However, the molecular mechanisms coupling functional and structural aspects of synaptic plasticity are still poorly defined. The catalytic activity of type I phosphoinositide-3-kinase (PI3K) is required for specific forms of synaptic plasticity, such as NMDA receptor-dependent long-term potentiation (LTP) and mGluR-dependent long-term depression (LTD). On the other hand, PI3K signaling has been linked to neuronal growth and synapse formation. Consequently, PI3Ks are promising candidates to coordinate changes in synaptic strength with structural remodeling of synapses. To investigate this issue, we targeted individual regulatory subunits of type I PI3Ks in hippocampal neurons and employed a combination of electrophysiological, biochemical and imaging techniques to assess their role in synaptic plasticity. We found that a particular regulatory isoform, p85α, is selectively required for LTP. This specificity is based on its BH domain, which engages the small GTPases Rac1 and Cdc42, critical regulators of the actin cytoskeleton. Moreover, cofilin, a key regulator of actin dynamics that accumulates in dendritic spines after LTP induction, failed to do so in the absence of p85α or when its BH domain was overexpressed as a dominant negative construct. Finally, in agreement with this convergence on actin regulatory mechanisms, the presence of p85α in the PI3K complex determined the extent of actin polymerization in dendritic spines during LTP. Therefore, this study reveals a molecular mechanism linking structural and functional synaptic plasticity through the coordinate action of PI3K catalytic activity and a specific isoform of the regulatory subunits.

Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties.

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles (GeO2 NP) embedded within a poly(methyl methacrylate) (PMMA) thin film. Besides exhibiting forming-free resistive switching and an uncommon "ON" state in pristine conditions, the hybrid (nanocomposite) devices demonstrate a unique form of mixed-mode switching. The observed stopping voltage-dependent switching enables state-of-the-art bifunctional synaptic behavior with short-term (volatile/temporal) and long-term (nonvolatile/nontemporal) modes that are switchable depending on the stopping voltage applied. The short-term memory mode device is demonstrated to further emulate important synaptic functions such as short-term potentiation (STP), short-term depression (STD), paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-voltage-dependent plasticity (SVDP), spike-duration-dependent plasticity (SDDP), and, more importantly, the "learning-forgetting-rehearsal" behavior. The long-term memory mode gives additional long-term potentiation (LTP) and long-term depression (LTD) characteristics for long-term plasticity applications. The work shows a unique coexistence of the two resistive switching modes, providing greater flexibility in device design for future adaptive and reconfigurable neuromorphic computing systems at the hardware level.

(Invited) Engineering the Switching Kinetics of Valence Change-Based Memristive Devices for Neuromorphic Computing

Memristive devices based on the valence mechanism are highly interesting candidates for the use as hardware representation of synapses in neuromorphic computing. The memristive model system SrTiO3 exhibit gradual switching and can be tuned between short-term and long-term plasticity. We will discuss the impact of the switching kinetics on the gradual switching mode and provide general guidelines for the design of gradual switching systems. Moreover, we present an approach to accelerate the switching kinetics of SrTiO3 by up to 103 times, or reduce the operating voltage by ≈ 30% to maintain the switching speed.Our approach is to introduce a low thermal conductivity layer inside the active electrode of the active electrode of the devices, which blocks the heat dissipation caused by Joule heating during switching. Our method leaves the switching layer and its interfaces with the electrodes intact, while the use of HfO2 and TaOx as the heat blocking layers ensures ease of fabrication and CMOS compatibility. We will demonstrate that this approach is transferable to other more common material systems.

The interaction between cannabinoids and long-term synaptic plasticity: A survey on memory formation and underlying mechanisms.

Synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), is an essential phenomenon in memory formation as well as maintenance along with many other cognitive functions, such as those needed for coping with external stimuli. Synaptic plasticity consists of gradual changes in the biochemistry and morphology of pre- and postsynaptic neurons, particularly in the hippocampus. Consuming marijuana as a primary source of exocannabinoids immediately impairs attention and working memory-related tasks. Evidence regarding the effects of cannabinoids on LTP and memory is contradictory. While cannabinoids can affect a variety of specific cannabinoid receptors (CBRs) and nonspecific receptors throughout the body and brain, they exert miscellaneous systemic and local cerebral effects. Given the increasing use of cannabis, mainly among the young population, plus its potential adverse long-term effects on learning and memory processes, it could be a future global health challenge. Indeed, the impact of cannabinoids on memory is multifactorial and depends on the dosage, timing, formula, and route of consumption, plus the background complex interaction of the endocannabinoids system with other cerebral networks. Herein, we review how exogenously administrated organic cannabinoids, CBRs agonists or antagonists, and endocannabinoids can affect LTP and synaptic plasticity through various receptors in interaction with other cerebral pathways and primary neurotransmitters.

Low-voltage solution-processed Sn-doped CuI neuromorphic transistors with synaptic plasticity and pain mimicked

In this article, SnxCu1−xI thin-film transistors were fabricated on a glass substrate, with CuI doped with varying concentrations of SnI2 serving as the channel and chitosan as the dielectric. When x = 0.06, the device exhibited optimal performance: a current on/off ratio of 2.56 × 105, a subthreshold slope of 31.67 mV/dec, a threshold voltage of 1.33 V, and a saturated field-effect mobility of 21.75 cm2 V−1 s−1. Due to the electric double layer effect of chitosan, the operating voltage of the devices was reduced to below 2 V. Simulations were also conducted on the behavior and functionality of artificial synapses, such as short-term plasticity, long-term plasticity, and paired-pulse facilitation. Building upon the functionalities of artificial synapses, the Sn0.06Cu0.94I neuromorphic transistors simulated the fundamental pain perception function of biological nociceptors. Finally, the effects of bias stress and laser irradiation on the devices were investigated, indicating the excellent stability of the Sn0.06Cu0.94I neuromorphic transistors. Fabricated via the solution process, this low-voltage neuromorphic transistors hold significant implications for applications in bionic sensing systems and neuromorphic chip technology.

Defect-engineering-based titanium dioxide nanowires for artificial optoelectronic synapses

Artificial optoelectronic synapses, as a typical neuromorphic device, have garnered significant attention due to their concurrent capabilities in processing visual perception and memory. In this study, we propose an artificial optoelectronic synapse with a dual-terminal metal-semiconductor-metal (Au/TiO2/Cr/Au) structure. By modulating the concentration of oxygen vacancy defects in TiO2, the device exhibits a significant persistent photoconductivity (PPC) effect. The devices successfully simulated the synaptic behaviors, including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), and the learning-forgetting process. In addition, the device array has the capability to recognize optical signals and exhibits an obvious response gradient under different optical powers. This demonstrates its ability to adaptively detect optical signals and simulate human visual memory.

Enhancement of Synaptic Performance through Synergistic Indium Tungsten Oxide-Based Electric-Double-Layer and Electrochemical Doping Mechanisms

This study investigated the potential of indium tungsten oxide (IWO) channel-based inorganic electrolyte transistors as synaptic devices. We comparatively analyzed the electrical characteristics of indium gallium zinc oxide (IGZO) and IWO channels using phosphosilicate glass (PSG)-based electrolyte transistors, focusing on the effects of electric-double-layer (EDL) and electrochemical doping. The results showed the superior current retention characteristics of the IWO channel compared to the IGZO channel. To validate these findings, we compared the DC bias characteristics of SiO2-based field-effect transistors (FETs) with IGZO and IWO channels. Furthermore, by examining the transfer curve characteristics under various gate voltage (VG) sweep ranges for PSG transistors based on IGZO and IWO channels, we confirmed the reliability of the proposed mechanisms. Our results demonstrated the superior short-term plasticity of the IWO channel at VG = 1 V due to EDL operation, as confirmed by excitatory post-synaptic current measurements under pre-synaptic conditions. Additionally, we observed superior long-term plasticity at VG ≥ 2 V due to proton doping. Finally, the IWO channel-based FETs achieved a 92% recognition rate in pattern recognition simulations at VG = 4 V. IWO channel-based inorganic electrolyte transistors, therefore, have remarkable applicability in neuromorphic devices.

Cortical plasticity is associated with blood-brain barrier modulation.

Brain microvessels possess the unique properties of a blood-brain barrier (BBB), tightly regulating the passage of molecules from the blood to the brain neuropil and vice versa. In models of brain injury, BBB dysfunction and the associated leakage of serum albumin to the neuropil have been shown to induce pathological plasticity, neuronal hyper-excitability, and seizures. The effect of neuronal activity on BBB function and whether it plays a role in plasticity in the healthy brain remain unclear. Here we show that neuronal activity induces modulation of microvascular permeability in the healthy brain and that it has a role in local network reorganization. Combining simultaneous electrophysiological recording and vascular imaging with transcriptomic analysis in rats, and functional and BBB-mapping MRI in human subjects, we show that prolonged stimulation of the limb induces a focal increase in BBB permeability in the corresponding somatosensory cortex that is associated with long-term synaptic plasticity. We further show that the increased microvascular permeability depends on neuronal activity and involves caveolae-mediated transcytosis and transforming growth factor β signaling. Our results reveal a role of BBB modulation in cortical plasticity in the healthy brain, highlighting the importance of neurovascular interactions for sensory experience and learning.

Cueing memory reactivation during NREM sleep engenders long-term plasticity in both brain and behaviour

Abstract Memory reactivation during Non-Rapid Eye Movement (NREM) sleep is important for memory consolidation but it remains unclear exactly how such activity promotes the development of a stable memory representation. We used Targeted Memory Reactivation (TMR) in combination with longitudinal structural and functional MRI to track the impact of reactivating memories in one night of sleep over the next 20 days. Our exploratory analysis showed that such cued reactivation leads to increased precuneus activation 24 h post-TMR. Furthermore, the behavioural impact of cueing, which only emerged 20 days later, was predicted by both functional and structural TMR related changes in sensorimotor cortex. These preliminary findings demonstrate that TMR leads to neuroplasticity, starting as early as 24 hours after the manipulation, and evolving over the next few weeks.

Wavelength‐Selective Photodetector and Neuromorphic Visual Sensor Utilizing Intrinsic Defect Semiconductor

AbstractWith the rapid developments of Artificial Intelligence (AI) and the Internet of Things (IoT), increasingly intricate and expanding application scenarios are placing higher demands on current machine vision capabilities. Therefore, there is a pressing need to simultaneously achieve diverse functionalities, simple designs, and efficient computing in vision devices. Here, the study develops a two‐terminal optoelectronic device utilizing a single 2D intrinsic defect semiconductor In2S3. The device demonstrates wavelength‐selective photodetection and neuromorphic visual capabilities, attributed to defect‐related charge‐trapping/de‐trapping processes. As a photodetector, the device exhibits a high photoresponsivity of 473.6 A W−1, a high external quantum efficiency of 1.6 × 105%, and a fast rise/fall time of 0.3/1.4 ms at the wavelength of 359 nm. As an all‐in‐one neuromorphic visual device, optoelectronic‐driven fundamental synaptic functions, including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), and “learning‐experience”, are successfully mimicked at the wavelength of 671 nm. Moreover, one‐shot recognition of the 12‐letter image “SHAN XI NORMAL” is achieved through an artificial convolutional neural network. This study provides a new strategy for developing compact high‐level intelligence systems for complex application scenarios.

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing.

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Predictors of long-term memory and network connectivity 10 years after anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) effectively controls seizures in medically refractory temporal lobe epilepsy but risks significant episodic memory decline. Beyond 1 year postoperatively, the influence of preoperative clinical factors on episodic memory and long-term network plasticity remain underexplored. Ten years post-ATLR, we aimed to determine biomarkers of successful memory network reorganization and establish presurgical features' lasting impact on memory function. Twenty-five ATLR patients (12 left-sided) and 10 healthy controls underwent a memory-encoding functional magnetic resonance imaging paradigm alongside neuropsychometry 10 years postsurgery. Generalized psychophysiological interaction analyses modeled network functional connectivity of words/faces remembered, seeding from the medial temporal lobes (MTLs). Differences in successful memory connectivity were assessed between controls and left/right ATLR. Multivariate regressions and mixed-effect models probed preoperative phenotypes' effects on long-term memory outcomes. Ten years post-ATLR, lower baseline functioning (verbal and performance intelligence quotient) and a focal memory impairment preoperatively predicted worse long-term memory outcomes. Poorer verbal memory was significantly associated with longer epilepsy duration and earlier onset age. Relative to controls, successful word and face encoding involved increased functional connectivity from both or remnant MTL seeds and contralesional parahippocampus/hippocampus after left/right ATLR. Irrespective of surgical laterality, successful memory encoding correlated with increased MTL-seeded connectivity to frontal (bilateral insula, right anterior cingulate), right parahippocampal, and bilateral fusiform gyri. Ten years postsurgery, better memory performance was correlated with contralateral frontal plasticity, which was disrupted with longer epilepsy duration. Our findings underscore the enduring nature of functional network reorganizations to provide long-term cognitive support. Ten years post-ATLR, successful memory formation featured stronger connections near resected areas and contralateral regions. Preoperative network disruption possibly influenced effectiveness of postoperative plasticity. These findings are crucial for enhancing long-term memory prediction and strategies for lasting memory rehabilitation.

CPEB2-activated axonal translation of VGLUT2 mRNA promotes glutamatergic transmission and presynaptic plasticity

BackgroundLocal translation at synapses is important for rapidly remodeling the synaptic proteome to sustain long-term plasticity and memory. While the regulatory mechanisms underlying memory-associated local translation have been widely elucidated in the postsynaptic/dendritic region, there is no direct evidence for which RNA-binding protein (RBP) in axons controls target-specific mRNA translation to promote long-term potentiation (LTP) and memory. We previously reported that translation controlled by cytoplasmic polyadenylation element binding protein 2 (CPEB2) is important for postsynaptic plasticity and memory. Here, we investigated whether CPEB2 regulates axonal translation to support presynaptic plasticity.MethodsBehavioral and electrophysiological assessments were conducted in mice with pan neuron/glia- or glutamatergic neuron-specific knockout of CPEB2. Hippocampal Schaffer collateral (SC)-CA1 and temporoammonic (TA)-CA1 pathways were electro-recorded to monitor synaptic transmission and LTP evoked by 4 trains of high-frequency stimulation. RNA immunoprecipitation, coupled with bioinformatics analysis, were used to unveil CPEB2-binding axonal RNA candidates associated with learning, which were further validated by Western blotting and luciferase reporter assays. Adeno-associated viruses expressing Cre recombinase were stereotaxically delivered to the pre- or post-synaptic region of the TA circuit to ablate Cpeb2 for further electrophysiological investigation. Biochemically isolated synaptosomes and axotomized neurons cultured on a microfluidic platform were applied to measure axonal protein synthesis and FM4-64FX-loaded synaptic vesicles.ResultsElectrophysiological analysis of hippocampal CA1 neurons detected abnormal excitability and vesicle release probability in CPEB2-depleted SC and TA afferents, so we cross-compared the CPEB2-immunoprecipitated transcriptome with a learning-induced axonal translatome in the adult cortex to identify axonal targets possibly regulated by CPEB2. We validated that Slc17a6, encoding vesicular glutamate transporter 2 (VGLUT2), is translationally upregulated by CPEB2. Conditional knockout of CPEB2 in VGLUT2-expressing glutamatergic neurons impaired consolidation of hippocampus-dependent memory in mice. Presynaptic-specific ablation of Cpeb2 in VGLUT2-dominated TA afferents was sufficient to attenuate protein synthesis-dependent LTP. Moreover, blocking activity-induced axonal Slc17a6 translation by CPEB2 deficiency or cycloheximide diminished the releasable pool of VGLUT2-containing synaptic vesicles.ConclusionsWe identified 272 CPEB2-binding transcripts with altered axonal translation post-learning and established a causal link between CPEB2-driven axonal synthesis of VGLUT2 and presynaptic translation-dependent LTP. These findings extend our understanding of memory-related translational control mechanisms in the presynaptic compartment.

Simulation of neural functions based on organic semiconductor/MXene synaptic transistors

Artificial synaptic devices, which are the basic units of neuromorphic computing systems, can perform signal processing with low power consumption. Organic synaptic transistors have attracted significant attention owing to their lightweight and good compatibility with flexible substrates. As per the current state of research both domestically and internationally, the majority of the existing organic synaptic transistors are based on floating gate structures, electret configurations, ferroelectric types. Furthermore, additional capture layers are required for the preparation of these devices. Two-dimensional MXenes have great potential in the preparation of synaptic transistors owing to their efficient multiple energy storage capabilities, excellent metallic conductivity, abundant surface functional groups, hydrophilicity, and layered structure. Therefore, high-performance synaptic transistors based on the two-dimensional material MXene were developed in this study. These transistors not only exhibited excellent memory performance with a memory window above 20 V, but also successfully simulated typical synaptic behaviors, including excitatory postsynaptic current/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/paired-pulse depression (PPF/PPD), and long-term plasticity (LTP). Synaptic transistors based on MXenes represent a promising approach for the preparation of high-performance organic synaptic transistors.

Multi-scale modelling of location- and frequency-dependent synaptic plasticity induced by transcranial magnetic stimulation in the dendrites of pyramidal neurons.

Repetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists.. We developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS. We implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations. We show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). This plasticity was strongly distance dependent and concentrated at the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression. The plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols.

Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between “0” and “1.”

Lasing‐Assisted Synthesis of Metal–Organic Frameworks (MOFs) and Its Application to Memory and Neuromorphic Devices

AbstractRecently, metal–organic frameworks (MOFs) have gained attention in the field of electronics owing to their capability to tune their electrical characteristics. However, conventional methods for synthesizing MOFs pose challenges for their integration into electronic devices because of their long synthesis times and complex transfer steps. In this study, for the first time, lasing‐assisted synthesis (LAS) is used to rapidly and directly synthesize MOFs. These are applied to resistive random access memory (RRAM) devices. Using the LAS method, Cu(BDC) and Cu(BTC) are synthesized in a remarkably short time (≈5 min) and formed directly on metal substrates as thin films. This simplified their integration into RRAMs. The Cu(BDC)‐ and Cu(BTC)‐based RRAMs are evaluated for their potential in memory and neuromorphic applications. Both devices demonstrated nonvolatile memory capabilities with a remarkable data retention time of 104 s and long‐term plasticity (LTP) in response to voltage stimuli. However, the suitability of each device for a specific application varies depending on the type of MOFs used. The Cu(BTC)‐based RRAM is more suitable for memory applications because of its higher on/off ratio, longer endurance, and more data storage capacity. Conversely, Cu(BDC)‐based RRAM is highly effective in neural network simulation, achieving higher classification accuracy.

Suppression of eEF2 phosphorylation alleviates synaptic failure and cognitive deficits in mouse models of Down syndrome.

Cognitive impairment is a core feature of Down syndrome (DS), and the underlying neurobiological mechanisms remain unclear. Translation dysregulation is linked to multiple neurological disorders characterized by cognitive impairments. Phosphorylation of the translational factor eukaryotic elongation factor 2 (eEF2) by its kinase eEF2K results in inhibition of general protein synthesis. We used genetic and pharmacological methods to suppress eEF2K in two lines of DS mouse models. We further applied multiple approaches to evaluate the effects of eEF2K inhibition on DS pathophysiology. We found that eEF2K signaling was overactive in the brain of patients with DS and DS mouse models. Inhibition of eEF2 phosphorylation through suppression of eEF2K in DS model mice improved multiple aspects of DS-associated pathophysiology including de novo protein synthesis deficiency, synaptic morphological defects, long-term synaptic plasticity failure, and cognitive impairments. Our data suggested that eEF2K signaling dysregulation mediates DS-associated synaptic and cognitive impairments. Phosphorylation of the translational factor eukaryotic elongation factor 2 (eEF2) is increased in the Down syndrome (DS) brain. Suppression of the eEF2 kinase (eEF2K) alleviates cognitive deficits in DS models. Suppression of eEF2K improves synaptic dysregulation in DS models. Cognitive and synaptic impairments in DS models are rescued by eEF2K inhibitors.

Possible mechanisms to improve sleep spindles via closed loop stimulation during slow wave sleep: A computational study.

Sleep spindles are one of the prominent EEG oscillatory rhythms of non-rapid eye movement sleep. In the memory consolidation, these oscillations have an important role in the processes of long-term potentiation and synaptic plasticity. Moreover, the activity (spindle density and/or sigma power) of spindles has a linear association with learning performance in different paradigms. According to the experimental observations, the sleep spindle activity can be improved by closed loop acoustic stimulations (CLAS) which eventually improve memory performance. To examine the effects of CLAS on spindles, we propose a biophysical thalamocortical model for slow oscillations (SOs) and sleep spindles. In addition, closed loop stimulation protocols are applied on a thalamic network. Our model results show that the power of spindles is increased when stimulation cues are applied at the commencing of an SO Down-to-Up-state transition, but that activity gradually decreases when cues are applied with an increased time delay from this SO phase. Conversely, stimulation is not effective when cues are applied during the transition of an Up-to-Down-state. Furthermore, our model suggests that a strong inhibitory input from the reticular (RE) layer to the thalamocortical (TC) layer in the thalamic network shifts leads to an emergence of spindle activity at the Up-to-Down-state transition (rather than at Down-to-Up-state transition), and the spindle frequency is also reduced (8-11 Hz) by thalamic inhibition.

Optoelectronic Synapse Behaviors in Tb3+ and Al3+ Co-Doped CaSnO3 with Long-Persistent Luminescence.

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo-synaptic behavior in the field of rare earth ion-doped luminescent oxides by using long-persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic-based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV-light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired-pulse facilitation, pulse-number/timing dependent potentiation, and pulse-number/timing dependent short- to long-term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli.