Long-term Plasticity Research Articles

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks.

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

CPT1C is required for synaptic plasticity and oscillatory activity that supports motor, associative and non-associative learning.

Carnitine palmitoyltransferase 1c (CPT1C) is a neuron-specific protein widely distributed throughout the CNS and highly expressed in discrete brain areas including the hypothalamus, hippocampus, amygdala and different motor regions. Its deficiency has recently been shown to disrupt dendritic spine maturation and AMPA receptor synthesis and trafficking in the hippocampus, but its contribution to synaptic plasticity and cognitive learning and memory processes remains mostly unknown. Here, we aimed to explore the molecular, synaptic, neural network and behavioural role of CPT1C in cognition-related functions by using CPT1C knockout (KO) mice. CPT1C-deficient mice showed extensive learning and memory deficits. The CPT1C KO animals exhibited impaired motor and instrumental learning that seemed to be related, in part, to locomotor deficits and muscle weakness but not to mood alterations. In addition, CPT1C KO mice showed detrimental hippocampus-dependent spatial and habituation memory, most probably attributable to inefficient dendritic spine maturation, impairments in long-term plasticity at the CA3-CA1synapse and aberrant cortical oscillatory activity. In conclusion, our results reveal that CPT1C is not only crucial for motor function, coordination and energy homeostasis, but also has a crucial role in the maintenance of learning and memory cognitive functions. KEY POINTS: CPT1C, a neuron-specific interactor protein involved in AMPA receptor synthesis and trafficking, was found to be highly expressed in the hippocampus, amygdala and various motor regions. CPT1C-deficient animals exhibited energy deficits and impaired locomotion, but no mood changes were found. CPT1C deficiency disrupts hippocampal dendritic spine maturation and long-term synaptic plasticity and reduces cortical γ oscillations. CPT1C was found to be crucial for motor, associative and non-associative learning and memory.

Tunable voltage polarity-dependent resistive switching characteristics by interface energy barrier modulation in ceria-based bilayer memristors for neuromorphic computing

Synaptic characteristics with tunable dependence on the voltage polarity are demonstrated in ceria (CeO2) and Gd-doped ceria (GDC) bilayer memristors with respect to their stacking orders. Both Pt/GDC/CeO2/Pt and Pt/CeO2/GDC/Pt memristors with different oxide stacking orders exhibit analog, linear, and symmetric synaptic weights for potentiation and depression, paired-pulse facilitation, and short- and long-term plasticity (STP and LTP, respectively). Potentiation and depression behaviors are highly linear and symmetric thanks to the stacking of more oxygen-deficient GDC layer with CeO2, which facilitates the redistribution of oxygen vacancies for analog resistance change. These memristors have opposite dependence of synaptic weight updates on the polarity of potentiation and depression voltages for the stacking order of CeO2 and GDC, which are consistently interpreted by voltage-driven energy barrier modulation at the interface between CeO2 and GDC or with Pt electrodes via oxygen vacancy redistribution. Recognition simulation with modified handwritten digits patterns using a two-layer perceptron neural network exhibits accuracy of approximately 88% with achieved dynamic range, linearity, symmetry, and precision states. These synaptic characteristics are also demonstrated in a 32 × 32 crossbar array of GDC-top memristors. This verifies the potential of bilayer memristors for application in artificial synapse networks in neuromorphic computing systems.

Implementation of Highly Stable Memristive Characteristics in an Organic–Inorganic Hybrid Resistive Switching Layer of Chitosan-Titanium Oxide with Microwave-Assisted Oxidation

This study proposes a high-performance organic–inorganic hybrid memristor for the development of neuromorphic devices in the memristor-based artificial synapse. The memristor consists of a solid polymer electrolyte (SPE) chitosan layer and a titanium oxide (TiOx) layer grown with a low-thermal-budget, microwave-assisted oxidation. The fabricated Ti/SPE–chitosan/TiOx/Pt-structured memristor exhibited steady bipolar resistive switching (BRS) characteristics and demonstrated excellent endurance in 100-cycle repetition tests. Compared to SPE–chitosan memristors without a TiOx layer, the proposed organic–inorganic hybrid memristor demonstrated a higher dynamic range and a higher response to pre-synaptic stimuli such as short-term plasticity via paired-pulse facilitation. The effect of adding the TiOx layer on the BRS properties was examined, and the results showed that the TiOx layer improved the chemical and electrical superiority of the proposed memristor synaptic device. The proposed SPE–chitosan organic–inorganic hybrid memristor also exhibited a stable spike-timing-dependent plasticity, which closely mimics long-term plasticity. The potentiation and depression behaviors that modulate synaptic weights operated stably via repeated spike cycle tests. Therefore, the proposed SPE–chitosan organic–inorganic hybrid memristor is a promising candidate for the development of neuromorphic devices in memristor-based artificial synapses owing to its excellent stability, high dynamic range, and superior response to pre-synaptic stimuli.

Asymmetric Voltage Attenuation in Dendrites Can Enable Hierarchical Heterosynaptic Plasticity.

Long-term synaptic plasticity is mediated via cytosolic calcium concentrations ([Ca2+]). Using a synaptic model that implements calcium-based long-term plasticity via two sources of Ca2+ - NMDA receptors and voltage-gated calcium channels (VGCCs) - we show in dendritic cable simulations that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at nonactivated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry can produce a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at the active synapses, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity.

New biophysical rate-based modeling of long-term plasticity in mean-field neuronal population models

The formation of customized neural networks as the basis of brain functions such as receptive field selectivity, learning or memory depends heavily on the long-term plasticity of synaptic connections. However, the current mean-field population models commonly used to simulate large-scale neural network dynamics lack explicit links to the underlying cellular mechanisms of long-term plasticity. In this study, we developed a new mean-field population model, the plastic density-based neural mass model (pdNMM), by incorporating a newly developed rate-based plasticity model based on the calcium control hypothesis into an existing density-based neural mass model. Derivation of the plasticity model was carried out using population density methods. Our results showed that the synaptic plasticity represented by the resulting rate-based plasticity model exhibited Bienenstock-Cooper-Munro-like learning rules. Furthermore, we demonstrated that the pdNMM accurately reproduced previous experimental observations of long-term plasticity, including characteristics of Hebbian plasticity such as longevity, associativity and input specificity, on hippocampal slices, and the formation of receptive field selectivity in the visual cortex. In conclusion, the pdNMM is a novel approach that can confer long-term plasticity to conventional mean-field neuronal population models.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex.

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

Sprague-Dawley rats differ in responses to medial perforant path paired pulse and tetanic activation as a function of sex and age.

Network plasticity in the medial perforant path (MPP) of adult (5-9 mo) and aged (18-20 mo) urethane-anesthetized male and female Sprague-Dawley rats was characterized. Paired pulses probed recurrent networks before and after a moderate tetanic protocol. Adult females exhibited greater EPSP-spike coupling suggesting greater intrinsic excitability than adult males. Aged rats did not differ in EPSP-spike coupling but aged females had larger spikes at high currents than males. Paired pulses suggested lower GABA-B inhibition in females. Absolute population spike measures were larger post-tetani in female rats than male rats. Relative population spike increases were greatest in adult males relative to females and to aged males. EPSP slope potentiation was detected with normalization in some post-tetanic intervals for all groups except aged males. Tetani shortened spike latency across groups. Tetani-associated NMDA-mediated burst depolarizations were larger for the first two trains in each tetanus in adult males than other groups. EPSP slopes over 30 min post-tetani predicted spike size in female rats, but not in males. Replicating newer evidence MPP plasticity in adult males was mediated by increased intrinsic excitability. Female MPP plasticity was related to synaptic drive increases, not excitability increases. Aged male rats were deficient in MPP plasticity.SIGNIFICANCE STATEMENTThe medial perforant path (MPP)-dentate gyrus (DG) granule cell synapse was the site of discovery of long-term potentiation in the mammalian nervous system but considering the current interest in sex and aging, surprisingly few studies have directly examined these variables in relation to tetanus-induced long-term and short-term (paired pulse) plasticity. Using an interleaved current-paired pulse interval protocol and moderate tetanic protocol young (5-9mo) and old (18-20mo) male and female urethane anesthetized rats were found to differ in levels of granule cell intrinsic excitability, E-S coupling, GABA-B inhibition, and tetanic NMDA current contributions to post-tetanic population spike potentiation. This study provides a platform or future examination of sex- and age-related changes in MPP-DG function.

Bionic optic nerve based on perovskite (CsPbBr 3) quantum-dots

The bionic optic nerve can mimic human visual physiology and is a future treatment for visual disorders. Photosynaptic devices could respond to light stimuli and mimic normal optic nerve function. By modifying (Poly(3,4-ethylenedioxythio-phene):poly (styrenesulfonate)) active layers with all-inorganic perovskite quantum dots, with an aqueous solution as the dielectric layer in this paper, we developed a photosynaptic device based on an organic electrochemical transistor (OECT). The optical switching response time of OECT was 3.7 s. To improve the optical response of the device, a 365 nm, 300 mW·cm -2 UV light source was used. Basic synaptic behaviors such as postsynaptic currents (0.225 mA) at a light pulse duration of 4 s and double pulse facilitation at a light pulse duration of 1 s and pulse interval of 1 s were simulated. By changing the way light stimulates, for example, by adjusting the intensity of the light pulses from 180 to 540 mW·cm -2, the duration from 1 to 20 s, and the number of light pulses from 1 to 20, the postsynaptic currents were increased by 0.350 mA, 0.420 mA, and 0.466 mA, respectively. As such, we realized the effective shift from short-term synaptic plasticity (100 s recovery of initial value) to long-term synaptic plasticity (84.3% of 250 s decay maximum). This optical synapse has a high potential for simulating the human optic nerve.

Superior artificial synaptic properties applicable to neuromorphic computing system in HfOx-based resistive memory with high recognition rates

With the development of artificial intelligence and the importance of big data processing, research is actively underway to break away from data bottlenecks and modern Von Neumann architecture computing structures that consume considerable energy. Among these, hardware technology for neuromorphic computing is in the spotlight as a next-generation intelligent hardware system because it can efficiently process large amounts of data with low power consumption by simulating the brain’s calculation algorithm. In addition to memory devices with existing commercial structures, various next-generation memory devices, including memristors, have been studied to implement neuromorphic computing. In this study, we evaluated the synaptic characteristics of a resistive random access memory (ReRAM) with a Ru/HfOx/TiN structure. Under a series of presynaptic spikes, the device successfully exhibited remarkable long-term plasticity and excellent nonlinearity properties. This synaptic device has a high operating speed (20 ns, 50 ns), long data retention time (> 2 h @85 ℃) and high recognition rate (94.7%). Therefore, we propose that memory and learning capabilities can be used as promising HfOx-based memristors in next-generation artificial neuromorphic computing systems.

Thermally Tunable Analog Memristive Behaviors in Sulfurized In2Se3 Nanoflakes for Bio-Plausible Synaptic Devices

Combining the intrinsic superiorities of two-dimensional materials and the emerging demand for neuromorphic computing, two-dimensional memristors have achieved huge advances in materials exploration and synaptic functionality emulation. However, their neuromorphic applications are still in the early stage since digital memristive behaviors for most of them are inconsistent with gradual biological synaptic plasticity. Here, we developed a simple approach to realize analog and thermal tunable memristive behaviors by introducing sulfur vacancies in CVD-grown In2Se3 nanoflakes through secondary sulfurization treatment. The density functional theory and ab initio molecular dynamics simulations confirm that sulfurized and defective In2Se3 can remain thermal stability at elevated temperatures up to 550 K. Therefore, we systematically investigated the temperature-dependent analog and tunable memristive behaviors and realized linear weight update with ultrawide dynamic range in sulfurized In2Se3 at high temperatures. The developed memristive device successfully emulates bio-realistic synaptic functionalities including transformation from short- to long-term plasticity, paired-pulse facilitation, posttetanic potentiation, spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and spike-time-dependent plasticity effects. It is unveiled that the formation energy of sulfur vacancy is greatly smaller than that of selenium, while their roughly same migration barriers can be modulated by electric field and temperature. Therefore, we put forward that the applied electric field can mediate vacancy migration in the sulfurized In2Se3 to gradually regulate the conductance, thereby realizing the emulation of synaptic plasticity. This work provides a promising approach to designing bio-plausible memristive devices for robust neuromorphic applications at high temperatures.

Involvement of P2Y1, P2Y6, A1 and A2A Receptors in the Purinergic Inhibition of NMDA-Evoked Noradrenaline Release in the Rat Brain Cortex.

In the cerebral cortex, glutamate activates NMDA receptors (NMDARs), localized in noradrenergic neurons, inducing noradrenaline release that may have a permissive effect on glutamatergic transmission, and therefore, on the modulation of long-term plasticity. ATP is co-released with noradrenaline, and with its metabolites (ADP and adenosine) is involved in the purinergic modulation of electrically-evoked noradrenaline release. However, it is not known if noradrenaline release evoked by activation of NMDARs is also under purinergic modulation. The present study aimed to investigate and to characterize the purinergic modulation of noradrenaline release evoked by NMDARs. Stimulation of rat cortical slices with 30 µM NMDA increased noradrenaline release, which was inhibited by ATP upon metabolization into ADP and adenosine and by the selective agonists of A1 and A2A receptors, CPA and CGS2680, respectively. It was also inhibited by UTP and UDP, which are mainly released under pathophysiological situations. Characterization of the effects mediated by these compounds indicated the involvement of P2Y1, P2Y6, A1 and A2A receptors. It is concluded that, in the rat brain cortex, NMDA-evoked noradrenaline release is modulated by several purinergic receptors that may represent a relevant mechanism to regulate the permissive effect of noradrenaline on NMDA-induced neuroplasticity.

Detection of Arc/Arg3.1 oligomers in rat brain: constitutive and synaptic activity-evoked dimer expression in vivo.

The immediate early gene product activity-regulated cytoskeleton-associated protein (Arc or Arg3.1) is a major regulator of long-term synaptic plasticity with critical roles in postnatal cortical development and memory formation. However, the molecular basis of Arc function is undefined. Arc is a hub protein with interaction partners in the postsynaptic neuronal compartment and nucleus. Previous in vitro biochemical and biophysical analysis of purified recombinant Arc showed formation of low-order oligomers and larger particles including retrovirus-like capsids. Here, we provide evidence for naturally occurring Arc oligomers in the mammalian brain. Using in situ protein crosslinking to trap weak Arc-Arc interactions, we identified in various preparations a prominent Arc immunoreactive band on SDS-PAGE of molecular mass corresponding to a dimer. While putative trimers, tetramers and heavier Arc species were detected, they were of lower abundance. Stimulus-evoked induction of Arc expression and dimer formation was first demonstrated in SH-SY5Y neuroblastoma cells treated with the muscarinic cholinergic agonist, carbachol, and in primary cortical neuronal cultures treated with brain-derived neurotrophic factor (BDNF). In the dentate gyrus (DG) of adult anesthetized rats, induction of long-term potentiation (LTP) by high-frequency stimulation (HFS) of medial perforant synapses or by brief intrahippocampal infusion of BDNF led to a massive increase in Arc dimer expression. Arc immunoprecipitation of crosslinked DG tissue showed enhanced dimer expression during 4 h of LTP maintenance. Mass spectrometric proteomic analysis of immunoprecipitated, gel-excised bands corroborated detection of Arc dimer. Furthermore, Arc dimer was constitutively expressed in naïve cortical, hippocampal and DG tissue, with the lowest levels in the DG. Taken together the results implicate Arc dimer as the predominant low-oligomeric form in mammalian brain, exhibiting regional differences in its constitutive expression and enhanced synaptic activity-evoked expression in LTP.

Linking input- and cell-type-specific synaptic plasticity to the reinforcement of alcohol-seeking behavior

The reinforcement of voluntary alcohol-seeking behavior requires dopamine-dependent long-term synaptic plasticity in the striatum. Specifically, the long-term potentiation (LTP) of direct-pathway medium spiny neurons (dMSNs) in the dorsomedial striatum (DMS) promotes alcohol drinking. However, it remains unclear whether alcohol induces input-specific plasticity onto dMSNs and whether this plasticity directly drives instrumental conditioning. In this study, we found that voluntary alcohol intake selectively strengthened glutamatergic transmission from the medial prefrontal cortex (mPFC) to DMS dMSNs in mice. Importantly, mimicking this alcohol-induced potentiation by optogenetically self-stimulating mPFC→dMSN synapse with an LTP protocol was sufficient to drive the reinforcement of lever pressing in operant chambers. Conversely, induction of a post-pre spike timing-dependent LTD at this synapse time-locked to alcohol delivery during operant conditioning persistently decreased alcohol-seeking behavior. Our results establish a causal relationship between input- and cell-type-specific corticostriatal plasticity and the reinforcement of alcohol-seeking behavior. This provides a potential therapeutic strategy to restore normal cortical control of dysregulated basal ganglia circuitries in alcohol use disorder.

Latent toxoplasmosis impairs learning and memory yet strengthens short-term and long-term hippocampal synaptic plasticity at perforant pathway-dentate gyrus, and Schaffer collatterals-CA1 synapses

Investigating long-term potentiation (LTP) in disease models provides essential mechanistic insight into synaptic dysfunction and relevant behavioral changes in many neuropsychiatric and neurological diseases. Toxoplasma (T) gondii is an intracellular parasite causing bizarre changes in host’s mind including losing inherent fear of life-threatening situations. We examined hippocampal-dependent behavior as well as in vivo short- and long-term synaptic plasticity (STP and LTP) in rats with latent toxoplasmosis. Rats were infected by T. gondii cysts. Existence of REP-529 genomic sequence of the parasite in the brain was detected by RT-qPCR. Four and eight weeks after infection, spatial, and inhibitory memories of rats were assessed by Morris water maze and shuttle box tests, respectively. Eight weeks after infection, STP was assessed in dentate gyrus (DG) and CA1 by double pulse stimulation of perforant pathway and Shaffer collaterals, respectively. High frequency stimulation (HFS) was applied to induce LTP in entorhinal cortex-DG (400 Hz), and CA3-CA1 (200 Hz) synapses. T. gondii infection retarded spatial learning and memory performance at eight weeks post-infection period, whereas inhibitory memory was not changed. Unlike uninfected rats that normally showed paired-pulse depression, the infected rats developed paired-pulse facilitation, indicating an inhibitory synaptic network disruption. T. gondii-infected rats displayed strengthened LTP of both CA1-pyramidal and DG-granule cell population spikes. These data indicate that T. gondii disrupts inhibition/excitation balance and causes bizarre changes to the post-synaptic neuronal excitability, which may ultimately contribute to the abnormal behavior of the infected host.

A Hebbian Model to Account for Musical Expertise Differences in a Working Memory Task

The TBRS*C computational model provides a mathematical implementation of the cognitive processes involved in complex span tasks. The logic of the core processes, i.e., encoding, refreshing/time-based decay, and chunking, is based on Hebbian learning, synaptic facilitation, and long-term neural plasticity, respectively. The modeling, however, takes place on a cognitive rather than a physiological level. Chunking is implemented as a process of searching for sequences of memoranda in long-term memory and recoding them as a single unit which increases the efficacy of memory maintenance. Using TBRS*C simulations, the present study investigated how chunking and central working memory processes change with expertise. Hobby musicians and music students completed a complex span task in which sequences of twelve note symbols were presented for serial recall of pitch. After the presentation of each memorandum, participants performed an unknown, notated melody on an electric piano. To manipulate the potential for chunking, we varied whether sequences of memoranda formed meaningful tonal structures (major triads) or arbitrary trichords. Hobby musicians and music students were each split up in a higher-expertise and a lower-expertise group and TBRS*C simulations were performed for each group individually. In the simulations, higher-expertise hobby musicians encoded memoranda more rapidly, invested less time in chunk search, and recognized chunks with a higher chance than lower-expertise hobby musicians. Parameter estimates for music students showed only marginal expertise differences. We conclude that expertise in the TBRS model can be conceptualized by a rapid access to long-term memory and by chunking, which leads to an increase in the opportunity and efficacy of refreshing.

Reconfigurable Low-Power TiO2 Memristor for Integration of Artificial Synapse and Nociceptor

Bio-mimetic advanced electronic systems are emerging rapidly, engrossing their applications in neuromorphic computing, humanoid robotics, tactile sensors, and so forth. The biological synaptic and nociceptive functions are governed by intricate neurotransmitter dynamics that involve both short-term and long-term plasticity. To emulate the neuronal dynamics in an electronic device, an Ag/TiO2/Pt/SiO2/Si memristor is fabricated, exhibiting compliance current controlled reversible transition of volatile switching (VS) and non-volatile switching (NVS). The origin of the VS and NVS depends on the diameter of the conducting filament, which is explained using a field-induced nucleation theory and validated by temporal current response measurements. The switching delay of the device is used to determine the characteristic nociceptive behaviors such as threshold, relaxation, inadaptation, allodynia, and hyperalgesia. The short-term and long-term retention loss attributed to the VS and NVS, respectively, is used to emulate short-term memory and long-term memory of the biological brain in a single device. More importantly, synergistically modulating the VS-NVS transition, the complex spike rate-dependent (SRDP) and spike time-dependent plasticity (STDP) with a weight change of up to 600% is demonstrated in the same device, which is the highest reported so far for TiO2 memristors. Furthermore, the device exhibits very low power consumption, ∼3.76 pJ/spike, and can imitate synaptic and nociceptive functions. The consolidation of complex nociceptive and synaptic behavior in a single memristor facilitates low-power integration of scalable intelligent sensors and neuromorphic devices.

Intermittent theta burst stimulation attenuates oxidative stress and reactive astrogliosis in the streptozotocin-induced model of Alzheimer's disease-like pathology.

Intracerebroventricularly (icv) injected streptozotocin (STZ) is a widely used model for sporadic Alzheimer's disease (sAD)-like pathology, marked by oxidative stress-mediated pathological progression. Intermittent theta burst stimulation (iTBS) is a noninvasive technique for brain activity stimulation with the ability to induce long-term potentiation-like plasticity and represents a promising treatment for several neurological diseases, including AD. The present study aims to investigate the effect of the iTBS protocol on the animal model of STZ-induced sAD-like pathology in the context of antioxidant, anti-inflammatory, and anti-amyloidogenic effects in the cortex, striatum, hippocampus, and cerebellum. Male Wistar rats were divided into four experimental groups: control (icv normal saline solution), STZ (icv STZ-3 mg/kg), STZ + iTBS (STZ rats subjected to iTBS protocol), and STZ + Placebo (STZ animals subjected to placebo iTBS noise artifact). Biochemical assays and immunofluorescence microscopy were used to evaluate functional and structural changes. The icv STZ administration induces oxidative stress and attenuates antioxidative capacity in all examined brain regions. iTBS treatment significantly reduced oxidative and nitrosative stress parameters. Also, iTBS decreased Aβ-1-42 and APP levels. The iTBS enhances antioxidative capacity reported as elevated activity of its enzymatic and non-enzymatic components. In addition, iTBS elevated BDNF expression and attenuated STZ-induced astrogliosis confirmed by decreased GFAP+/VIM+/C3+ cell reactivity in the hippocampus. Our results provide experimental evidence for the beneficial effects of the applied iTBS protocol in attenuating oxidative stress, increasing antioxidant capacity and decreasing reactive astrogliosis in STZ-administrated rats.

Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization.

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Lactoferrin Modulates Induction of Transcription Factor c-Fos in Neuronal Cultures

Lactoferrin (Lf) is a multifunctional protein from the transferrin family. Of particular interest is the ability of Lf to affect a wide range of neuronal processes by modulating the expression of genes involved in long-term neuroplasticity. The expression of the immediate early gene c-fos that is rapidly activated in response to external influences, and its product, transcription factor c-Fos, is widely used as a marker of long-term neuronal plasticity. The present study aims to examine the effect of human Lf on the induction of transcription factor c-Fos in the primary mouse neuronal cultures after stimulation and to determine the cellular localization of human Lf and its colocalization with induced c-Fos protein. Primary dissociated cultures of hippocampal cells were obtained from the brains of newborn C57BL/6 mice (P0-P1). On day 7 of culturing, human Lf was added to the medium. After 24 h (day 8 in culture), c-Fos protein was induced in cells by triple application of 50 mM KCl. c-Fos content was analyzed using the immunofluorescent method 2 h after stimulation. Stimulation promoted exogenous Lf translocation into the nuclei of cultured neuronal cells, which correlated with increased induction of transcription factor c-Fos and was accompanied by nuclear colocalization of these proteins. These results attest to the potential of Lf as a modulator of neuronal processes and open up new prospects in studying the mechanisms of the regulatory effects of lactoferrin on cell function.