14 results about "Postsynapse" patented technology

This invention relates to the field of neuromorphic computing technology and provides a method and system for dynamic adjustment of synaptic weights in real-time neurofeedback for neuromorphic chips. The method includes: capturing the pulse signals and timestamps emitted by presynaptic and postsynaptic neurons; calculating the time difference between the presynaptic and postsynaptic pulses; when the absolute value of the time difference is less than a preset time window threshold, querying a pulse timing dependency plasticity rule base based on the sign of the time difference to determine the corresponding synaptic weight adjustment type; generating corresponding voltage pulse parameters based on the adjustment type and the current conductance state of the target memristor synapse; and applying a write voltage pulse to the target memristor synapse according to the voltage pulse parameters to adjust its conductance value in situ in real time, thereby dynamically updating the synaptic weights. This invention solves the problems of poor dynamic environment adaptability, low energy efficiency, and high learning latency caused by traditional offline weight update mechanisms.

The application provides a brain-like synapse learning method and a brain-like technology neural morphological hardware system, and the method comprises the following steps: determining a pulse pair generated by a presynaptic neuron and a postsynaptic neuron in a brain-like synapse learning circuit, wherein the pulse pair comprises an input pulse generated by the presynaptic neuron and an output pulse generated by the postsynaptic neuron; determining an STDP mechanism corresponding to the pulse pair and a synapse weight corresponding to the STDP mechanism based on the pulse pair; and performing STDP learning corresponding to the brain-like synapse learning circuit based on the pulse pair and the synapse weight; wherein the STDP mechanism is a pulse time-dependent plasticity mechanism, and the front and rear pulses of the pulse pair correspond to a long-term potentiation process or a long-term depression process in the STDP mechanism according to the time sequence. The application realizes online learning of brain-like intelligence, and plays the environment self-adaptive characteristics of brain-like computing.

The present invention provides a multiomics approach, which integrate single-cell RNA-sequencing (scRNA-seq) and spatiotemporal transcriptomics (ST) offering potential for dissecting transcriptional networks and revealing cell-cell interactions involved in biomolecular pathomechanisms. The present invention also provides a multimodal approach combining high-throughput scRNA-seq and ST to elucidate XLRS-specific transcriptomic signatures in two XLRS-like models with retinal splitting phenotypes, including genetically engineered (Rs1emR209C) mice and patient-derived retinal organoids harboring the same patient-specific p.R209C mutation. Through multiomics transcriptomic analysis, the endoplasmic reticulum (ER) stress / eIF2 signaling, mTOR pathway, and the regulation of eIF4 and p70S6K pathways as chronically enriched and highly conserved disease pathways between two XLRS-like models are identified. Western blots and proteomics analysis validated the occurrence of unfolded protein responses, chronic eIF2α signaling activation, and chronic ER stress-induced apoptosis. Furthermore, therapeutic targeting of the chronic ER stress / eIF2α pathway activation synergistically enhanced the efficacy of AAV mediated RS1 gene delivery, ultimately improving bipolar cell integrity, postsynaptic transmission, disorganized retinal architecture and electrophysiological responses. Collectively, the complex transcriptomic signatures obtained from Rs1emR209C mice and patient-derived retinal organoids using the multiomics approach provide opportunities to unravel potential therapeutic targets for incurable retinal diseases, such as XLRS.

There is provided a neurosynaptic processing core with spike time dependent plasticity (STDP) learning for a spiking neural network, including: a spiking neuron block including a pre-synaptic block and a post-synaptic block; a synapse block communicatively coupled to the spiking neuron block; a STDP learning block communicatively coupled to the spiking neuron block and the synapse block, the STDP learning block including a pre-synaptic event accumulator including a pre-synaptic spike event memory block and a pre-synaptic spike parameter modifier; a post-synaptic event accumulator including a post-synaptic spike event memory block and a post-synaptic spike parameter modifier, a weight change accumulator, and a weight change parameter modifier; a learning error modulator; and a synaptic weight modifier configured to modify a synaptic weight parameter based on a weight change parameter and a learning error corresponding to the synaptic weight parameter. There is also provided a corresponding method of operating and a corresponding method of forming the neurosynaptic processing core.

The application discloses a kind of video physiological signal dynamic extraction method and device, collect video and carry out face detection and posture correction to each frame video;The corrected face image is divided into micro-grid, and the brightness of each micro-grid is response-delay coding, and the pulse sequence of presynaptic pulse neuron is generated;Through membrane potential model and mutual inhibition between pulse neurons, competition is carried out and combined with connection weight, and the pulse emission time and pulse frequency of postsynaptic pulse neuron are obtained;According to the time difference between presynaptic and postsynaptic pulse neuron, dynamically adjust connection weight;After sorting and screening the pulse frequency of all postsynaptic neurons, it is mapped to the pulse sequence formed by corresponding micro-grid and weighted fusion, and the final pulse wave signal is generated;Finally, the physiological parameters of practitioner are obtained by calculating pulse wave signal, and the connection weight is adjusted by signal quality evaluation, and the strategy optimization of pulse wave signal extraction is realized continuously.

A synapse surgical tool for selectively removing or ablating a postsynaptic terminal from a neuron, the method comprising: delivering an expression vector comprising a nucleic acid encoding a fusion protein comprising an N-terminal domain comprising an activated glial receptor binding domain; and a C terminal domain comprising a transmembrane domain of postsynaptic protein; expressing the fusion protein so that the activated glial receptor binding domain is localized to a synaptic cleft of the postsynaptic terminal of the neuron; and contacting the neuron with an activated microglial cell so that the activated microglial binds to the activated glial receptor binding domain and selectively ablates the postsynaptic terminal of the neuron.

A neuromorphic device includes: a neuron block unit including a plurality of neurons; a synapse block unit including a plurality of synapses; and a topology block unit including a plurality of parallel Look-Up Table (LUT) modules including pre and post neuron elements configured with addresses of a presynaptic neuron and a postsynaptic neuron. Each of the plurality of neurons has an intrinsic address, each of the plurality of synapses has an intrinsic address. The parallel LUT module is partitioned based on a first synapse address among synapse addresses, and each of the partitions is indexed based on a second synapse address among the synapse addresses.

The application discloses a terahertz neural synapse memorization device and a preparation method thereof. The terahertz neural synapse memorization device comprises a substrate, an active region formed on the substrate, two electrodes in the form of interdigital electrodes, the two electrodes comprising test parts and finger parts, the test parts of the two electrodes being respectively formed on two sides of the active region, the finger parts being arranged on the active region in a staggered manner at a certain interval, and the interval between adjacent finger parts being controlled at a nanometer level; the two electrodes are respectively used as a presynaptic end and a postsynaptic end of a neural synapse, a high-frequency voltage signal is applied to the presynaptic end as an excitation source of the neural synapse, and a current signal response of the postsynaptic end is collected, so that a terahertz neural morphological calculation function is realized.

The invention discloses a memristor neural network circuit based on emotion influence STDP learning rate, which realizes biological synaptic plasticity through resistance plasticity and threshold value characteristics of a memristor, and simulates the regulation effect of emotion on brain learning rate. The circuit is composed of a pre-synaptic neuron, a post-synaptic neuron, a pulse module and a sensitive module. Wherein the pre-synaptic neurons and the post-synaptic neurons receive environment input signals and emotion signals, and the input mood signals are learned by using the weight adjustment module and the memristor; the pulse module compares a difference value between the neuron output signal and the emotion signal, and outputs a pulse signal when the difference value is lower than a threshold voltage; the sensitive module dynamically adjusts the resistance value of the memristor according to the pulse signal so as to simulate the emotion, thereby adjusting the weight and adjusting the voltage. When the emotion is excited, the learning rate is increased; when the emotion is low, the learning rate is reduced, and the circuit effectively simulates the influence of the emotion on the STDP learning rate.

The present invention relates to the field of drug development and neuropharmacology. One aspect of this invention involves a method for screening substances that increase the stability of excitatory synaptic connections under destabilizing conditions. This method entails exposing a tested compound to an in vitro differentiated neuron culture along with a destabilization compound, followed by incubation with the test compound. Subsequently, a postsynaptic parameter is measured in the neurons, and this measurement is compared to that of neurons not exposed to the tested substance. This high-content screening system represents a significant advancement in neuropharmacology and drug discovery, offering automated and high-throughput capabilities for assessing the effects of chemical compounds on neuronal connections in vitro. Another aspect of the invention pertains to the use of dilazep, penbutolol, and their salts for alleviating and treating symptoms associated with depressive disorders.

The present invention relates to the field of drug development and neuropharmacology. One aspect of this invention involves a method for screening substances that increase the stability of excitatory synaptic connections under destabilizing conditions. This method entails exposing a tested compound to an in vitro differentiated neuron culture along with a destabilization compound, followed by incubation with the test compound. Subsequently, a postsynaptic parameter is measured in the neurons, and this measurement is compared to that of neurons not exposed to the tested substance. This high-content screening system represents a significant advancement in neuropharmacology and drug discovery, offering automated and high-throughput capabilities for assessing the effects of chemical compounds on neuronal connections in vitro. Another aspect of the invention pertains to the use of dilazep, penbutolol, and their salts for alleviating and treating symptoms associated with depressive disorders.

The present invention discloses a neurosynaptic structure for a spiking neural network, wherein the neurosynaptic structure comprises: one or more input ports, one or more synaptic elements, each synaptic element connected to at least one of the input ports and configured to receive an input signal and to output a weighted postsynaptic signal. Furthermore, the neurosynaptic structure comprises a neuron connected to the one or more synaptic elements. The neurosynaptic structure is provided with different feedback and control structures such as AMPA-, GABA-, and NMDA receptors, axon-, dendrite- and neuron back-propagation channels and / or an astrocytes structure.

The invention relates to the technical field of neural networks, and discloses a spiking neural network acceleration method and device, equipment, a storage medium and a product, and the method comprises the steps: carrying out the descending sorting of pre-synaptic neurons according to the weight; gradually accumulating the weights of the activated pre-synaptic neurons into the membrane potential of the post-synaptic neurons according to the sorting sequence; in the step-by-step accumulation process, when the membrane potential of the post-synaptic neurons is larger than the excitation threshold potential, the post-synaptic neurons output pulses so as to improve the calculation efficiency.

A method for processing data based on a neural network including a first layer including axons and a second layer including neurons, includes receiving synaptic weights between the first layer and the second layer; generating presynaptic weights, a number of which is identical to a number of the axons, and postsynaptic weights, a number of which is identical to a number of the synaptic weights, from the synaptic weights; and storing the presynaptic weights and the postsynaptic weights in a synapse memory.