161 results about "Synaptic plasticity" patented technology

The invention discloses bifidobacterium breve and application thereof in relieving Alzheimer's disease, and belongs to the technical field of probiotics. The preparation method comprises the following steps: separating 18 bifidobacterium strains from feces of children aged 0-3 years old, analyzing and identifying the 18 bifidobacterium strains including bifidobacterium longum, bifidobacterium breve and bifidobacterium animalis, and screening out the bifidobacterium breve LE4 with potential Alzheimer's disease relieving capacity through in-vitro oxidation resistance and LPS (Lipopolysaccharide) removal capacity. The method comprises the steps of behavioral experiments (nesting experiments, open field experiments and water maze experiments), histopathologic analysis, molecular biological detection and the like. The multi-target AD intervention effects of improving the cognitive function, reducing A beta deposition and tau protein phosphorylation, relieving neuroinflammation, enhancing synaptic plasticity, repairing intestinal barriers, adjusting intestinal flora and metabolites and the like of the LE4 strain are comprehensively evaluated, and a new strategy is provided for promoting AD micro-ecological treatment.

The invention relates to a novel pulse neural network synaptic weight adjustment method, and belongs to the field of artificial intelligence. Firstly, a traditional pulse time sequence dependence plasticity rule is improved based on the distribution trend of a Gaussian function, so that the weight change corresponding to the optimized synaptic plasticity rule when the time interval tends to 0 is larger, and when the time interval is gradually increased, the weight change is in an accelerated descending trend; the method comprises the following steps: firstly, performing synaptic weight local regulation on a neuron model to improve the local regulation capability of the synaptic plasticity of the neuron, then introducing reinforcement learning, associating Q learning with the synaptic plasticity in the neuron model, and realizing the global regulation and control of the plasticity on the basis of the synaptic weight local regulation. According to the above synaptic plasticity rule, the spiking neural network constructed based on Leaky Integrating and Fire is subjected to weight adjustment, so that the spiking neural network has better performance.

The invention relates to the technical field of electronic devices, in particular to a synaptic memristive device with a vertical structure and a preparation method and application of the synaptic memristive device. The invention provides a synaptic memristor with a vertical structure. The synaptic memristor structurally comprises a substrate, a bottom electrode, an active layer, an electrolyte layer and a top electrode which are stacked in sequence. Wherein the active layer is a vanadium pentoxide film which is excellent in electrical property and optical response, and the sensitivity and plasticity of the device can be improved; the electrolyte layer adopts polyethylene oxide ionic glue, so that the resistance change characteristic of the active layer can be adjusted, the reaction between the active layer and the top electrode can be prevented, and the service life of the device is prolonged; the bottom electrode is an indium tin oxide thin film, has good compatibility with an active layer interface, can promote oxygen vacancy migration, and optimizes the resistance switching performance. Through mutual coordination of the materials, the device can generate microampere-level current under millivolt-level stimulation, shows high-sensitivity synaptic plasticity, and is suitable for a high-performance neuromorphic computing system.

The invention provides a multiband photoresponse nerve synapse device based on a two-dimensional and organic heterostructure, an array and application, and belongs to the technical field of organic electric solid-state devices. The device provided by the invention adopts a three-end bottom gate top contact structure and comprises a gate substrate, an insulating layer, a channel layer and a source / drain electrode, and the channel layer forms an II-type heterojunction by a two-dimensional semiconductor material with a narrow-band gap characteristic and an organic semiconductor material. The heterostructure realizes ultraviolet to far infrared (365nm-10 [mu] m) multiband photoelectric response, and shows synaptic plasticity under light pulse modulation, including short-term enhancement, long-term enhancement and learning-forgetting-re-learning characteristics. Large-area array preparation of the device can be realized through methods such as chemical vapor deposition, solution spin coating, jet printing or layer-by-layer self-assembly. The technology not only can be used for brain-like learning and memory simulation, but also can be applied to the fields of sensing and memory integrated intelligent systems, optical neural networks, artificial vision, multi-mode intelligent sensing and the like.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses a big data analysis system and method for a financial guarantee circle, and relates to the technical field of data analysis, and the method comprises the steps: collecting industrial and commercial, judicial and financial data in real time through an API gateway, and constructing a quantization guarantee network model and a quantum annealing parameter configuration file after preprocessing; a quantum annealing machine is used for executing hidden relation detection, and Bayesian confidence coefficient calibration is combined to generate a high-association guarantee ring list and a quantum calculation log; simulating a risk conduction path based on a synaptic plasticity model of the spiking neural network, and outputting a synaptic weight change table and a dynamic conduction diagram; the risk levels are mapped into a hierarchical blocking strategy matrix through a dynamic blocking decision engine, topology reinforcement is carried out by combining historical data and adopting a minimum spanning tree algorithm, and a stable subnet list and a protection path diagram are generated; and finally, performing weighted fusion on the quantum log, the conduction model and the subnet data, constructing a three-dimensional risk map, and optimizing a real-time decision by using a long and short-term memory network and an attention mechanism network.

The invention discloses a multi-modal sensory artificial synaptic device based on a framework material and a preparation method and application thereof. The artificial synaptic device comprises a substrate and a channel, wherein the channel is arranged on the surface of the substrate; the channel is an amorphous organic metal framework film which responds to light and gas stimulation; the metal electrode is arranged on the surface of the channel; the metal electrode comprises a source electrode and a drain electrode. Compared with the prior art, the framework material with excellent photoelectric response performance is adopted, and a novel artificial synapse device with multi-mode sensing ability is constructed by fully utilizing the regular pore structure, large specific surface area and good photoresponse characteristic of the MOF material. The two electrodes of the device are connected into electrical test equipment, the conductivity of the amorphous MOF thin film can be changed under the stimulation of optical pulse signals, and finally simulation based on photoresponse synaptic plasticity is achieved. The device is placed in different gas environments, and the conductivity of the MOF film can be changed by capturing and releasing target gas molecules through the porous characteristic of the amorphous MOF material, so that simulation based on gas response synaptic plasticity is realized.

The invention relates to the field of photoelectric material and device physics, and discloses a preparation method of a Ga2O3: Sn / HfOx heterojunction photoelectric sensor, and the method comprises the following steps: S1, cleaning a substrate, S2, preparing a Ga2O3: Sn thin film, S3, growing an electrode, S4, preparing an HfOx thin film, and carrying out neural synapse bionic application of the Ga2O3: Sn / HfOx heterojunction photoelectric sensor. Comprising Ga2O3: Sn / HfOx synaptic plasticity research and Ga2O3: Sn / HfOx photoelectric sensor sensing and storage calculation integrated application research. The Ga2O3: Sn thin film is prepared by adopting a sol-gel method with accurate and controllable doping amount, donor doping with controllable concentration is realized by controlling the mass of the added tin sheet, so that the photocurrent and responsivity of the photoelectric sensor are increased, the HfOx thin film is prepared by adopting a magnetron sputtering method, the photoelectric sensor with a double-layer structure is constructed, the detection rate and responsivity of the device are improved, and the photoelectric sensor is suitable for the field of photoelectric sensors. An ultraviolet signal is used to simulate a plurality of synaptic functions, including EPSC, PPF and empirical learning behaviors. Furthermore, a high-linearity light-enhanced electrical suppression behavior is realized, and a basis is provided for realizing a high-precision image recognition function.

The invention discloses an analog memristor based on a Ga2O3 / TiO2 heterojunction structure and a preparation method of the analog memristor, and belongs to the field of nonvolatile resistive random access memorizers, the preparation method comprises the following steps: under a vacuum condition, depositing a conductive film on a pretreated insulating substrate by adopting a physical vapor deposition method to serve as a bottom electrode; sequentially depositing a Ga2O3 thin film and a TiO2 thin film on the bottom electrode by adopting a magnetron sputtering method, and constructing a Ga2O3 / TiO2 heterojunction as a resistive layer; and depositing a conductive thin film on the surface of the resistive layer by adopting a physical vapor deposition method to serve as a top electrode to obtain the analog memristor based on the Ga2O3 / TiO2 heterojunction structure. A Ga2O3 semiconductor material with a wide forbidden band and high electron mobility and a TiO2 material with a high dielectric constant, stability and reliability are adopted, and the excellent and high-stability heterojunction memristor is prepared through a specific sputtering process. Bionic behaviors such as synaptic plasticity can be realized, and the method has important application potential in the fields of high-density storage, neural network calculation, biological synaptic simulation and the like.

The invention relates to a device and a method for distinguishing adaptive behaviors of a mouse musical scale based on discrete frequency acoustic stimulation. The device and the method are composed of an acoustic stimulation generation module, a behavior response acquisition module and a self-adaptive decision control module, an acoustic stimulation sequence of discrete frequency scales is acquired, and a multi-modal parameter coordinated regulation and control system is constructed in combination with an Arduino development environment; on the basis of an embedded signal control circuit, an autonomously designed closed-loop feedback control unit and a multi-channel acoustic signal generation device are integrated, and a musical scale-behavior associated quantitative model based on operational conditioned reflex is established through a two-stage experimental process of'progressive adaptive training-stepped threshold verification '. The method has the function of synchronously detecting auditory cortex frequency coding precision and prefrontal cortex decision ring synaptic plasticity, and a novel experimental tool is provided for quantitative evaluation of neurodegenerative disease central auditory dysfunction and research on a decision-related neural circuit plasticity mechanism.

The invention relates to the technical field of working face mine pressure multi-parameter intelligent monitoring and early warning systems, and particularly discloses a working face mine pressure multi-parameter intelligent monitoring and early warning system. The system comprises a multi-source sensing data acquisition device, a pulse sequence coding unit, a bionic pulse neural network early warning model, a dynamic attention regulation and control module and an early warning information output terminal, multi-source heterogeneous data such as hydraulic support resistance, micro-seismic energy and acoustic emission frequency are converted into a pulse event sequence similar to a biological neural signal, a rock stratum instability precursor is identified by using a bionic model simulating a neuron discharge mechanism and synaptic plasticity, key signals are self-adaptively strengthened in combination with a dynamic attention mechanism, and noise is suppressed. According to the technical scheme, multi-parameter coupling characteristics can be captured with high sensitivity, the early warning accuracy and advance are improved, the false alarm rate is reduced, and reliable guarantee is provided for deep coal mine safety.

The invention discloses a method and system for improving image classification performance by using a spiking neural network, and the method comprises the steps: focusing on a neuron activation mode and synaptic plasticity in an unsupervised handwritten numeral recognition research process based on a triple pulse time-dependent plasticity learning rule, and after the fluctuation of accuracy is observed, carrying out the recognition of an unsupervised handwritten numeral recognition model, and carrying out the recognition of the unsupervised handwritten numeral recognition model. According to the method, key influence factors such as weight initialization, STDP randomness and hyper-parameter sensitivity are deeply analyzed; according to the invention, a common SNN structure is optimized, a pulse Inception module is realized, and the SNN is trained through a triple STDP; according to the method, the simulated annealing algorithm and the STDP are innovatively combined to be used for neuron parameter optimization, an optimal hyper-parameter combination is established through SA, after STDP training, the performance is remarkably improved under the condition of a limited data set, and particularly, the high efficiency of one-time perceptual classification of hundreds of digits is shown.

The invention discloses a synaptic plastic mechanism bionic circuit based on a threshold symmetric memristor, and relates to the technical field of bionic circuits, the bionic circuit comprises a pre-synaptic neuron circuit, the threshold symmetric memristor and a post-synaptic neuron circuit; the bionic circuit is divided into an excitatory synaptic moldability mechanism circuit and an inhibitory synaptic moldability mechanism circuit according to a synaptic moldability mechanism; the design method of the circuit comprises the following steps: firstly, designing neuron waveforms before and after synapse, then traversing changes of memristive synapse conductance values at different time intervals to obtain a corresponding curve of the conductance changes and the time intervals, verifying the fitting degree of a biological synapse plastic mechanism curve and the curve, and if the fitting degree is low, determining that the biological synapse plastic mechanism curve is not matched with the curve. And if not, redesigning the pulse waveforms before and after synapse. The synaptic plasticity mechanism simulated by the bionic circuit provided by the invention and a biological synaptic STDP mechanism model have a small fitting error, the provided design method is higher in universality, circuit parameters can be adjusted according to different memristors, and different forms of biological synaptic plasticity mechanisms can be simulated.

The invention discloses a pulse neural network error back propagation training method based on local synaptic plasticity, and the method comprises the steps: 1, building a pulse response model, taking a pulse sequence before and after synaptic neurons discharge as input, calculating a post-synaptic potential through a synaptic kernel function and weighted summation, and enabling the post-synaptic neurons to generate a new pulse as output; 2, simplifying the post-synaptic potential into two independent components by refining the pulse discharge time and approximately calculating the post-synaptic potential boundary value; 3, approximating a derivative between the post-synaptic potential and pulse counting by using pulse time dependent plasticity through a simplified post-synaptic potential calculation formula; and 4, combining local synaptic plasticity with a global optimization mechanism through a pulse time dependent plasticity approximate unknown derivative. According to the method, the advantages of local synaptic plasticity and a global optimization mechanism are fully utilized, the learning and adaptive capacity of the spiking neural network is enhanced, the method is closer to a biological nervous system, and the classification precision on training static and neuromorphic data sets is improved.

The invention provides an organic photoelectrochemical synaptic transistor and a preparation method and a regulation and control method thereof, and belongs to the technical field of transistors. The transistor comprises a substrate, a gate electrode, a source electrode, a drain electrode, an organic semiconductor channel layer and an electrolyte layer; according to the invention, various synaptic plasticity functions can be realized on a single synaptic transistor device through a simple and efficient preparation process; the method has the advantages of simplicity, high efficiency and convenience in large-scale integration. The synergistic effect of photons, electrons and ions is used as a regulation and control mechanism, a single-component n-type organic semiconductor is used as a channel layer material, voltage programming pulses are applied to a grid electrode, light irradiation is applied to a channel layer to simulate biological synaptic stimulation, output current is obtained at a source electrode and a drain electrode to simulate post-biological synaptic current, and the potential safety hazard is eliminated. Information encryption, compiling, hiding and recovery of the Morse code are simulated based on the output pulse current, and the method can be widely applied to the brain-like intelligence field of cognitive judgment with information double anti-counterfeiting encryption.

The invention belongs to the technical field of electronic devices, and particularly relates to an artificial synapse transistor based on multiple layers of ITZO thin films and a preparation method and application thereof. The invention discloses an artificial synapse transistor based on a multilayer ITZO film. The artificial synapse transistor structurally comprises a substrate, an active layer, a source-drain electrode layer, a passivation layer and a gate dielectric layer which are stacked in sequence. Wherein the active layer is composed of multiple layers of ITZO thin films, and the thickness of the active layer is regulated to be 28-36 nm, so that the surface defects of the thin films are greatly reduced, and the interface quality of the active layer of the transistor is remarkably optimized. Not only is the mobility of the device greatly improved, but also the operation stability and the synaptic plasticity of the device are remarkably enhanced. Benefited from the ingenious combination of the materials, the synaptic transistor shows excellent electrical properties, opens up a new application prospect for a plurality of fields such as bionic sensing devices, neuromorphic memories and brain-like computing chips, and shows huge potential and value.

The invention relates to an artificial visual nervous system based on a bimodal neural device, which comprises a bimodal visual information calculation array composed of a physical convolution kernel array and a synaptic calculation array, and is characterized in that the physical convolution kernel array is used for receiving an ambient light signal and converting the ambient light signal into an electric signal for output; the synaptic calculation array is used for receiving the electric signals and converting the electric signals into visual signals, and the physical convolution kernel array and the synaptic calculation array are each composed of a plurality of bimodal transistors. The method has excellent bimodal cooperative computing capability, and realizes image feature extraction and preprocessing by simulating a receptive field mechanism (excitement / inhibitory response) of the retina bipolar cells through the physical convolution kernel array; the synaptic plasticity (LTP / LTD mechanism) of a central nervous system is simulated in combination with a synaptic calculation array, advanced calculation of visual signals is completed, and a complete optic nerve bionic system is formed by means of the array composed of single devices.

The present invention proposes a fast memory encoding method based on a multi-synaptic plasticity pulse neural network, including: step one: converting external stimuli into an input pulse sequence based on a hierarchical encoding strategy; step two: after the pulse neural network receives the input pulse, the membrane potential of the output layer neurons is updated based on the improved SRM model; step three: using the supervised group Tempotron to update the synaptic weights between the input and the output layer, and activating the output layer neurons to memorize the input; step four: after the output layer neurons are activated, using unsupervised STDP to update the synaptic weights between the activated neurons in the layer to form an enhanced recurrent subnetwork to store memory; step five: while executing step four, using unsupervised inhibition of synaptic plasticity to update the synaptic weights between the inhibition layer and the output layer, and inhibiting feedback to ensure the separation of the firing time of neural groups with different inputs. The present invention also proposes a fast memory encoding device based on a multi-synaptic plasticity pulse neural network. The present invention effectively improves the encoding speed and stability of memory.

The present application relates to a nutritional composition, a product comprising the same and use thereof in the preparation of a product for alleviating synaptic plasticity impairment of a body. The nutritional composition comprises: docosahexaenoic acid (DHA) and / or a metabolic precursor thereof, sodium hyaluronate (SH), and lactoferrin (LF). When docosahexaenoic acid (DHA), sodium hyaluronate (SH) and lactoferrin (LF) are used in combination, they can synergistically alleviate synaptic plasticity impairment of a body, in particular, increase nerve growth factor (NGF) content and / or brain-derived neurotrophic factor (BDNF) content in PC12 cells of the body, and / or increase expression of synaptic-related proteins in PC12 cells of the body.

This invention relates to the fields of semiconductor information storage and artificial synaptic devices, specifically to a biological synaptic intrinsically self-assembled topological phase-change memristor and its fabrication method. The fabrication method includes: providing a substrate; depositing a bottom electrode; and depositing SrFeO on the bottom electrode. x The thin film serves as the storage medium layer; the storage medium layer undergoes heat treatment, including in-situ annealing and post-annealing. By controlling the oxygen pressure difference between in-situ and post-annealing, the single-component SrFeO is made more efficient. x An intrinsically self-assembled superlattice structure, characterized by alternating vertical and horizontal orientation regions of oxygen vacancy channels, spontaneously forms within the thin film. This structure restricts the formation sites of conductive filaments, significantly improving the consistency and stability of the device's resistive switching cycle. The process of this invention is simple, requiring no introduction of a second-phase material. The resulting memristor requires no electrical formation, exhibits a high on / off ratio, multi-level storage, and short-term synaptic plasticity, making it applicable to neuromorphic computing chips and artificial intelligence devices.

The invention discloses a novel active peptide composition for improving concentration and a preparation method and application thereof. Belongs to the technical field of new active peptide composition preparation. The novel active peptide composition is prepared through synergism of the novel active peptide PAVP, the blood-brain barrier penetrating peptide and the metabolism regulator, when the novel active peptide PAVP, the blood-brain barrier penetrating peptide and the metabolism regulator act together, the novel active peptide composition has a synergistic effect, and the concentration improving effect of the novel active peptide composition is better than that of each independent component; the time for the mouse to explore new things can be prolonged to a certain extent, the relative expression quantity of synaptic plasticity markers PSD-95, BDNF and p-CREB can be increased to a certain extent, and technical support is provided for research and development of concentration-related products.

The invention discloses an application of a pyruvate dehydrogenase kinase (PDKS) inhibitor in preparation of a composition for treating Alzheimer's disease (AD), and particularly discloses an application of the PDKs inhibitor in preparation of the composition for treating the AD. According to the invention, the level of oxidative phosphorylation (OXPHOS) of microglial cells is obviously enhanced, the inflammatory response of the microglial cells is effectively reduced, the migration and aggregation capabilities of the microglial cells are obviously improved, the capability of the microglial cells to phagocytize and degrade extracellular beta-amyloid protein (A beta) plaque is obviously enhanced, the cognitive function defect of AD model mice is effectively improved, and the application of the microglial cells in the treatment of the AD model mice is promoted. The preparation is simple, and the application is wide.

The application discloses a kind of artificial synapse devices with color / electric dual weight regulation.The composition of the device includes: the surface edge of substrate, source electrode and drain electrode are respectively distributed with interval, and the surface of substrate between source electrode and drain electrode is covered with nanowire film;Nafion film and ion glue are sequentially covered on nanowire film.The device obtained by the application can realize the dual regulation of color weight and conductance weight under electric stimulation, and make the two weights have extremely short synaptic plasticity, and after removing stimulation for only 1 second, excitatory chroma is completely reduced to 0, and residual excitatory post-synaptic current is reduced to 1 / 3000 of initial value.

This invention discloses a method for screening or assessing the risk of GLP-1 receptor agonist-related mood disorders. It reveals for the first time that TTR is a key molecular switch for the bidirectional mood effects of GLP-1 receptor agonists, and its expression level needs to be maintained within a specific physiological window to ensure mood homeostasis. This invention assesses the risk of developing mood disorders after using GLP-1 receptor agonists by detecting the expression level or activity of TTR in subjects. This invention also provides kits for assessing the mood side effects of GLP-1 receptor agonists, methods for screening candidate drugs for the prevention or treatment of related mood disorders, and pharmaceutical compositions containing TTR or its agonists. This invention elucidates the molecular mechanism by which GLP-1 receptor agonists inhibit TTR expression through the cAMP / PKA / Gli3 signaling pathway, thereby affecting the ERK / CREB pathway and synaptic plasticity, providing novel targets and strategies for precise drug use, risk warning, and intervention for related mood disorders using GLP-1 receptor agonists.

The invention discloses an intelligent hardware configuration optimization method based on deep learning. The method comprises the following steps of collecting system operation data and performing preprocessing; generating a high-dimensional input feature matrix by using an input feature analysis module of the improved NCGM model; introducing a microsynaptic plasticity mechanism to form initial local performance characterization; introducing a continuous time topological evolution mechanism, and constructing a dynamic topological structure; forming a global performance representation vector; adopting a multi-domain collaborative adaptive optimization mechanism to obtain a convergence parameter set; and outputting a hardware configuration parameter set, thereby realizing intelligentization and adaptive configuration optimization of the target hardware system, and improving the self-learning ability, performance prediction precision and energy consumption utilization rate of the hardware system.

The present invention relates to polymorphs of a nitrogen-containing heterocyclic compound. The polymorphs of the present invention can effectively enhance or reactivate synaptic plasticity, and inhibit or eliminate the accumulation of beta-amyloid, without causing side effects, thereby exerting a therapeutic effect on various nervous system diseases or neurodegenerative diseases. In addition, the polymorphs of the present invention exhibit excellent physicochemical properties, such as stability, solubility, hygroscopicity, and mechanical strength, and show excellent permeability to the blood-brain barrier (BBB).

The invention discloses a novel brain protein enzymolysis active peptide for improving concentration and application thereof. Belongs to the technical field of new active peptide preparation. According to the invention, fresh pig brain is used as a raw material for enzymolysis and purification to successfully prepare the novel active peptide PAVP through brain protein enzymolysis. Efficacy verification is further carried out on the oligopeptide PAVP, and experimental results show that the oligopeptide PAVP can remarkably prolong the time of exploration of mice on new things in a new things recognition experiment and improve the concentration degree; in order to further verify the action mechanism of the PAVP, the expression quantities of synaptic plasticity markers PSD-95, BDNF and p-CREB in a mouse body are detected on the molecular level, and experimental results show that the PAVP not only repairs the synaptic touchable injury, but also possibly further enhances the synaptic function so as to achieve the purpose of improving the concentration degree.

The present invention relates to a synergistic composition for the treatment of disorders related to central nervous system (CNS) excitability. The synergistic composition comprises combination of GABA receptor agonists and synaptic plasticity enhancer or salts thereof along with pharmaceutically acceptable excipients. The GABA receptor agonists are selected from the group consisting of 4-[(2R)-2,4-Dihydroxy-3,3-dimethylbutanamido]butanoic acid, 4- (Pyridine-3-carbonylamino)butanoic acid and 4-(hexadecanoylamino)butanoic acid or salts thereof. The synaptic plasticity enhancer are selected from magnesium 2- acetamidoethanesulfonate, magnesium bis (2-aminoethane-1-sulfonate), magnesium;(2S,3R)- 2,3,4-trihydroxybutanoate, N-(2-Hydroxyethyl)hexadecanamide, 1-(4-aminobutyl)guanidine and 5'-inosinic acid or salts thereof.

The present invention relates to a nitrogen-containing heterocyclic compound derivative and its use. The compound according to the present invention can effectively improve synaptic plasticity without side effects, thereby exerting therapeutic effects on various neurological disorders such as neurodegenerative diseases, neurodegenerative pathologies, neuropsychiatric disorders, memory loss, cognitive dysfunction / injury, and extinction learning disorders.

The invention belongs to the technical field of artificial intelligence, and particularly discloses a cross-modal injury perception associative memory circuit based on an AIST memristor, and the circuit comprises an injury perception module which is used for converting touch, visual and auditory multi-modal injury signals into corresponding pulse voltage signals; the associative learning module is used for simulating synaptic plasticity through a memristor and strengthening the associative memory of a cross-modal signal; when the multi-modal damage signals are input at the same time, the resistance value of the memristor is reduced, the connection weight between neurons is enhanced, and associated storage and mutual activation of cross-modal signals are realized; the generalization differentiation module is used for extracting common characteristics of cross-modal signals, enabling similar stimuli to trigger the same response, and distinguishing signal specificity characteristics by using a resistance state separation mechanism of a memristor; and the output neuron module is used for generating a final response signal according to the processing result of the generalization differentiation module and simulating the stress response of the organism. The biological nervous system information processing mechanism can be comprehensively simulated.