82 results about "Membrane potential" patented technology

The invention discloses a liquid state machine dynamics optimization system based on a self-feedback mask, and the system comprises a liquid state machine reservoir which is provided with a plurality of spike neurons; the liquid state machine reservoir updates the membrane potential state of the spiking neuron by receiving the time sequence input signal; the mask generation module is connected with a membrane potential state of a spiking neuron in a reservoir of the liquid state machine and is used for generating a self-feedback mask according to the membrane potential state; and the feedback adjusting unit is respectively connected with the mask generation module and the liquid state machine reservoir and is used for adjusting the circulating connection and the self-feedback calculation of the liquid state machine reservoir according to the self-feedback mask. The dynamic stability of the liquid state machine and the sufficiency of time sequence information utilization can be optimized, and the performance and robustness of the liquid state machine can be improved.

The invention discloses a myocardial transmembrane potential segmented time sequence reconstruction method based on a physical information neural network, and the method improves the model generalization ability: a physical information data generation step, especially a diversified generation strategy, can create large-scale training data covering wide physiological and pathological states, and improves the model generalization ability. According to the method, a deep learning model can learn robustness characterization of various complex electrocardio phenomena, and the generalization ability of the model and the applicability of the model in a real scene are greatly improved. According to the composite loss function, especially a physical consistency loss item, the physical law describing propagation of an electric signal from the heart to the body surface serves as a soft constraint to be embedded into the training process, a solution output by a forcing network must be capable of'explaining 'observed body surface potential BSP data, and the BSP data can be used as a soft constraint. The method greatly reduces the understanding space, and effectively inhibits the generation of artifacts and wrong solutions which do not accord with physical laws.

The invention relates to the technical field of bionic control, and discloses a robot control method based on a pulse neural network, and the method comprises the steps: carrying out the spatial-temporal feature coding of the environment perception information and body state information of a robot, and obtaining a pulse feature sequence; performing pulse dependence fusion on the pulse characteristic sequence and the feedback pulse signal to obtain a bionic information processing process, and performing membrane potential integration on the bionic information processing process to obtain an integrated output quantity; performing pulse distribution processing on the integrated output quantity to obtain a driving pulse signal; performing path fitting on the driving pulse signal to obtain a continuous motion track; based on the continuous motion track and the bionic information processing process, performing joint space mapping on the continuous motion track to obtain an execution control instruction; the application executes the control instruction, synchronously collects a pulse response signal and feeds back the pulse response signal in real time so as to obtain an optimization control instruction of the robot; the pulse neural network-based robot control efficiency can be improved.

A neural network device according to an embodiment includes a plurality of synapse circuits and a plurality of neuron circuits. A first neuron circuit out of the neuron circuits includes an input circuit, a charge holding circuit, a comparison circuit, a firing circuit, a charge control circuit, and a control signal output circuit. When a determination signal changes from a second value to a first value, the firing circuit outputs a spike signal. The control signal output circuit outputs a control signal indicating a comparison voltage that is based on an excess component of a membrane potential exceeding a threshold potential. In response to acquiring the spike signal from the first neuron circuit, a first synapse circuit out of the synapse circuits that acquires the spike signal from the first neuron circuit outputs a synaptic current of a current amount corresponding to the control signal and a synaptic weight.

This invention discloses a method for constructing a spiking neural network based on recurrent firing neurons, comprising: S1: constructing a recurrent firing spiking neuron model; S2: calculating positive and negative pulses based on the gradient function corresponding to each time step in the recurrent firing spiking neuron model to obtain cross-time-step positive and negative pulses; S3: backpropagating the cross-time-step positive and negative pulses back through the recurrent firing spiking neuron model to obtain a spiking neural network; S4: training the spiking neural network using a positive and negative membrane potential balance loss function to obtain a recurrent firing spiking neural network. This invention significantly improves the learning ability and information representation ability of the spiking neural network, while enhancing its robustness.

This invention discloses a liquid state machine dynamics optimization system based on a self-feedback mask, comprising a liquid state machine reservoir with multiple spike neurons; the liquid state machine reservoir updates the membrane potential state of the spike neurons by receiving time-series input signals; a mask generation module, connected to the membrane potential state of the spike neurons in the liquid state machine reservoir, is used to generate a self-feedback mask based on the membrane potential state; a feedback adjustment unit, connected to both the mask generation module and the liquid state machine reservoir, is used to adjust the cyclic connections and self-feedback calculations of the liquid state machine reservoir based on the self-feedback mask. This invention can optimize the stability of liquid state machine dynamics and the sufficiency of time-series information utilization, thereby improving the performance and robustness of the liquid state machine.

An artificial neuron includes a first capacitive node of application of a membrane potential of the neuron. A first transistor is configured to discharge the first capacitive node. A second capacitive node is driven according to the membrane potential and delivers a potential for controlling the first transistor. A second transistor is configured to discharge the second capacitive node. The second transistor is controlled according to a potential present at the second capacitive node.

The application provides a kind of method for enhancing robustness of pulse neural network based on neural activation and connection optimization, applied to artificial intelligence and neural network technical field, the method comprises: constructing pulse neural network model, the neuron of pulse neural network model adopts burst enhancement type pulse neuron, the input of pulse neural network model is image data, and the output of pulse neural network model is the image processing result corresponding to computer vision task;When the membrane potential of burst enhancement type pulse neuron exceeds the firing threshold, the part exceeding the membrane potential is converted into the pulse output within the burst window by quantization linear mapping function, and the number of pulse output is an integer between 0 and the preset maximum burst pulse number;When training pulse neural network model, add activation perception regularization term to total loss function;Through the application, the accuracy and robustness can be simultaneously improved while maintaining the high energy efficiency advantage of pulse neural network.

The present application relates to the field of artificial neuromorphics, and provides an artificial neuron circuit based on a volatile threshold switching device. A membrane potential generation circuit is used for generating different membrane potentials under different input currents and different external reset voltages; a slow variable generation circuit is used for generating a slow variable; a membrane potential reset circuit comprises a volatile threshold switching device and an external reset voltage excitation source; the external reset voltage excitation source is used for providing different external reset voltages, so that the membrane potential and the slow variable are changed, and a spiking behavior of biological neurons is simulated; and the volatile threshold switching device is used for realizing a membrane potential reset function. Compared with traditional CMOS-circuit-based neurons, the neuron circuit provided by the present application requires a greatly reduced number of transistors and reduced hardware and power consumption overhead, has the characteristics of simple structure, high flexibility and rich functions, and is beneficial to large-scale integration in a hardware pulse neural network.

The invention relates to the field of artificial intelligence and the field of integrated circuits, in particular to a chip for in-memory calculation of a spiking neural network, which comprises a synaptic weight storage array for storing synaptic weights; the threshold storage array is used for storing a neuron threshold voltage; the membrane potential storage array is used for storing a neuron membrane potential, a bit line of the membrane potential storage array is connected with a bit line of the synaptic weight storage array, and logic calculation results of the synaptic weight and the neuron membrane potential are directly generated on the connected bit lines; the membrane potential accumulation unit obtains a membrane potential accumulation result through processing of an addition logic circuit; the comparison unit is used for opening read sub-lines of the membrane potential storage array and the threshold voltage storage array and multiplexing an addition logic circuit to realize subtraction calculation of a membrane potential accumulation result and a threshold voltage; and the transmitting unit is used for judging whether the neurons output pulses or not and resetting the membrane potential. The method can achieve the push-training integration of in-memory calculation, remarkably improves the SNN operation energy efficiency, and reduces the power consumption.

The invention discloses a low-voltage nanosecond pulse platform waveform fitting method, and belongs to the technical field of high-frequency pulse control, and the method comprises the steps: obtaining a set voltage value, calculating the optimal pulse width corresponding to a target cell through a transmembrane voltage formula according to the set voltage value, and calculating the optimal pulse width according to a dose and transmembrane potential accumulation model. Calculating the number of equivalent nanosecond pulse sub-pulses capable of replacing the optimal pulse width, calculating the number of pulse strings according to the total dose set by the optimal pulse width and the number of the equivalent nanosecond pulse sub-pulses, fitting nanosecond pulses, and replacing the optimal pulse width, the calculated nanosecond fitting value, the calculated pulse string number and preset parameters are stored in a storage chip of the pulse platform, the pulse platform calls the storage parameters for energy emission, the low-voltage nanosecond platform can be used for achieving the combined effect of a microsecond platform and a high-voltage nanosecond platform, released bubbles are smaller than those of the microsecond platform, the occurrence rate of embolism is reduced, and the service life of the microsecond platform is prolonged. The released voltage is lower than that of traditional nanoseconds, and the problems of blood fusion and smooth muscle and nerve damage are reduced.

The invention discloses an FPGA-based nerve cell membrane potential detection method and a related device, and the device comprises an input excitation module which is used for inputting an analog current signal to an FPGA core processing module; a model IP core is deployed in the FPGA core processing module, a pipeline design is adopted in the model IP core, the model IP core is obtained by mapping a Hodgkin-Here differential equation corresponding to the Purkinje cell electrophysiology model through high-level synthesis, the Purkinje cell membrane potential value at the current moment is calculated in real time according to an analog current signal, and the Purkinje cell membrane potential value is calculated in real time according to the calculated Purkinje cell membrane potential value. A digital voltage signal is obtained; the digital-to-analog conversion module is used for receiving the digital voltage signal output by the FPGA core processing module and converting the digital voltage signal into an analog voltage signal; and the display module is used for displaying the membrane potential waveform in real time according to the analog voltage signal. On the basis, the real-time high-speed calculation of the membrane potential change can be realized, and the occupation of hardware resources is effectively reduced.

Disclosed herein are system, method, and computer program embodiments for an improved spiking neural network (SNN) configured to learn and perform unsupervised, semi-supervised, and supervised extraction of features from an input dataset. An embodiment operates by receiving a modification request to modify a base neural network, having N layers and a plurality of spiking neurons, trained using a primary training dataset. The base neural network is modified to include supplementary spiking neurons in the Nth or N+1th layer of the base neural network. The embodiment includes receiving a secondary training dataset and determining membrane potential values of one or more supplementary spiking neurons in the Nth or Nth+1 layer which learn features based on secondary training data set to select a supplementary / winning spiking neuron. The embodiment performs a learning function for the modified neural network based on the winning spiking neuron.

This invention provides a method, system, electronic device, and storage medium for analyzing the energy consumption of neural networks based on ion channel regulation. It constructs a neural network dynamics model containing excitatory and inhibitory neurons. The neuronal membrane potential dynamics equation is defined by a set of differential equations for various voltage-gated ion currents (fast sodium ion current, continuous sodium ion current, slow potassium ion current, etc.) and leakage current. By adjusting the unit area conductivity parameter and / or inactivation time constant parameter of the voltage-gated ion channels in the model, the external intervention effect is simulated. After obtaining membrane potential sequence data through simulation, the total energy consumption, synaptic energy consumption ratio, average discharge rate, and average synchronization rate between neurons of the model are calculated. Finally, a quantitative correlation model between parameter changes and the above indicators is established, clarifying the coupling effect of the target ion channel dynamics characteristics on the neural network discharge behavior pattern and energy metabolism.

The invention relates to a piezoelectric nano-material with ultrasonic responsiveness, which is a PVP (Polyvinyl Pyrrolidone) modified bismuth oxyiodate nano-sheet, the particle size is 100-200 nm, and a surface electric field can be generated under the ultrasonic action. The piezoelectric nano material can hyperpolarize the potential of a T cell membrane and inhibit an ion channel Kv1.3 regulated and controlled by the membrane potential, so that Th17 cell differentiation is reduced, and joint inflammation is relieved; the piezoelectric nano material is further prepared into a joint injection preparation, and the joint injection preparation can be combined with ultrasonic irradiation to be applied to treatment of rheumatoid arthritis. According to the invention, the piezoelectric nano material is successfully applied to immune microenvironment regulation and control of rheumatoid arthritis for the first time, and the product and technology blank in the field of'precisely regulating and controlling immune cell functions by using physical signals to treat autoimmune diseases' is filled.

Minimize the loss of information transmitted. [Solution] The neural network device according to the embodiment comprises a plurality of synaptic circuits and a plurality of neuron circuits. The first neuron circuit among the plurality of neuron circuits is supplied with synaptic current to its first terminal from each of the first synaptic circuits among the plurality of synaptic circuits. The first neuron circuit has a charge storage circuit, a spike output circuit and a cutoff circuit. The charge storage circuit stores charge according to the synaptic current and generates a membrane potential according to the stored charge. The spike output circuit outputs a spike signal when the membrane potential is greater than a preset threshold potential. The cutoff circuit stops the supply of synaptic current from the first terminal to the charge storage circuit during a cutoff period, which is a predetermined time after the spike signal is output.

The embodiment of the invention provides an artificial neural network model conversion method, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: after obtaining an artificial neural network model obtained through pre-training, converting each nonlinear operator in the artificial neural network model into a corresponding pulse module, each pulse module comprises a difference expectation compensation module, the difference expectation compensation module is used for calculating an output increment according to the accumulated membrane potential and inserting a difference pulse neuron into each pulse module, the difference pulse neuron updates a coding activation value when issuing a pulse, otherwise, the coding activation value is kept unchanged, and the difference expectation compensation module is used for outputting the difference pulse neuron. According to the method, the bias term of the linear operator located on the previous layer of each nonlinear operator is removed, the initial membrane potential of the differential pulse neuron inserted into the pulse module corresponding to the nonlinear operator is set as the bias term, and the coding activation value is updated only when the pulse is emitted, so that the loss caused by updating the coding activation value no matter whether the pulse is emitted or not is avoided, and the accuracy of the coding activation value is improved. And the energy consumption is obviously reduced.

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.

The specification discloses a training method and device of a reserve pool calculation model based on a pulse signal, comprising: inputting historical environment images as training samples into each neuron of a hidden layer of a reserve pool calculation model based on a pulse signal to be trained. According to the annotation of the training sample, a target vector is determined. For each neuron, according to the pulse signals fired by other neurons at the last moment and the initial connection weight, the current connection weight of other neurons to the neuron is determined, and other input potentials of other neurons input into the neuron are determined. According to the time scale of the decay of the membrane potential of the neuron, a decay potential is determined. According to the decay potential, the other input potentials and the training sample, a state vector composed of the membrane potentials of each neuron is determined. According to the state vector and the target vector, the readout weight of the readout layer is calculated, so that the trained model has good robustness, high output result accuracy and can achieve the expected effect.

The invention discloses a spiking neural network inference device and method supporting elastic inference, and belongs to the field of artificial intelligence hardware acceleration, the device comprises a plurality of nerve cores and a network-on-chip NoC, and each nerve core comprises a processing unit PE, a router and an output scheduler. The router binds a plurality of pulse events generated by the same spine / token in the same row into a binding address event representing a BAER data unit and transmits the BAER data unit in the NoC; and after the target nerve nucleus is unpacked, an output scheduler immediately triggers subsequent layer calculation according to spine / token-level fine-grained flowing water. And the PE adopts a small-batch pulse Gustavson product, only reads and writes back to a corresponding membrane potential / tracker row once for each BAER batch, and accumulates a plurality of weight rows in parallel, thereby supporting addition and subtraction updating of polarity pulses. According to the scheme, the first response delay is reduced, and the communication and memory access energy consumption is reduced.

The embodiment of the invention generally relates to the technical field of bioengineering, in particular to a nucleotide construct, an adeno-associated virus vector, and a system and a method for detecting neural activity. The nucleotide construct comprises an open reading frame, and the open reading frame comprises a voltage response domain coding sequence and a sound wave modulation domain coding sequence. The nucleotide construct is configured to be expressed on an astrocyte membrane under the control of a promoter so as to provide a recombinant protein with a voltage response domain and an acoustic wave modulation domain, so that the recombinant protein can generate corresponding reversible conformation change in response to membrane potential change of the astrocyte, the acoustic characteristics of the recombinant protein can be changed by the reversible conformation change. In this way, the recombinant protein can sense changes in membrane potential and modulate the reflection characteristics to ultrasonic waves.

The application discloses a myocardial transmembrane potential segmented time sequence reconstruction method based on a physical information neural network, and improves model generalization capability: a physical information data generation step, in particular, a diversified generation strategy, can create large-scale training data covering a wide range of physiological and pathological states, which enables a deep learning model to learn robust representation of various complex electrocardio phenomena, greatly improving the generalization capability of the model and applicability in real scenes. Guaranteeing physical authenticity of solution, suppressing artifacts: a composite loss function, in particular, a 'physical consistency loss term' therein, which embeds the physical law describing the propagation of an electric signal from a heart to a body surface into a training process as a soft constraint, forces the solution output by the network to be able to 'explain' observed body surface potential BSP data, greatly narrows down the solution space, and effectively suppresses the generation of artifacts and false solutions that do not conform to physical laws.

The invention discloses a digital spiking neuron, a spiking neural network and an implementation method thereof, and relates to the technical field of neuromorphic calculation and brain-like chips. The digital spiking neuron comprises a synaptic component, a neuron body component and a weight storage component, wherein the neuron body component and the weight storage component are respectively connected with the synaptic component; wherein the synaptic component is used for detecting an input pulse event, generating a dual-channel synaptic current and performing current weighting on the dual-channel synaptic current based on weight data to form a weighted current, and the dual-channel synaptic current comprises a fast channel synaptic current and a slow channel synaptic current; the neuron body component is used for performing membrane potential accumulation and leakage updating on the weighted current, and outputting a pulse signal under the condition of meeting a threshold triggering condition and having no shaft hill suppression; the weight storage component is used for storing weights from other digital spiking neurons to the digital spiking neurons in a distributed manner, and providing weights for current weighting for the synaptic component.

The invention relates to the technical field of neuron circuits, and provides a configurable neuron circuit and a control method, and the circuit comprises a mode selection module which selects a corresponding target working mode according to a received neuron configuration signal; the calculation module is connected to the mode selection module, and performs corresponding membrane potential updating according to the target working mode selected by the mode selection module and the received pulse information of the presynaptic neurons to obtain a membrane potential updating result; the comparison module is connected to the calculation module and is used for comparing the membrane potential updating result with a preset threshold value and determining whether the current neuron can generate a pulse or not according to a comparison result; and the pulse generation module is connected to the comparison module, and is used for generating a pulse signal and an AER (Advanced Encryption Register) coding group package and outputting updated membrane potential information when the current neuron is determined to generate the pulse. According to the technical scheme, selection of two neuron models can be achieved, the circuit structure is simplified, and meanwhile more complex behavior modes are achieved.

The invention discloses a nerve stress injury identification algorithm based on multi-attention crossing, and relates to the technical field of artificial intelligence and biomedical engineering crossing. According to the algorithm, an end-to-end differentiable joint optimization framework is constructed, firstly, learnable self-adaptive preprocessing including band-pass filtering, empirical mode decomposition and dynamic threshold denoising is carried out on original electroencephalogram signals; secondly, designing a time, space and frequency three-path parallel attention mechanism, and realizing dynamic weighted integration of multi-dimensional features through a cross gating fusion module; and finally, adopting a spiking neural network with a learnable membrane potential attenuation coefficient and a dynamic threshold to carry out brain-like decision, outputting a damage probability, positioning a thermodynamic diagram and carrying out severity score. All module parameters are jointly optimized under a unified loss function, co-evolution of signal enhancement, feature fusion and a decision-making mechanism is achieved, and the accuracy, robustness and interpretability of neural stress injury recognition are remarkably improved.

This invention discloses a SAR image target detection and recognition method based on a brain-like deep spiking neural network. It converts SAR images into a pulse event stream format suitable for SNN processing through direct input encoding. An improved membrane potential quantization scheme is employed to reduce quantization errors in the spiking neuron layers. A shallow feature extraction module (LLF-SNN) and a deep feature extraction module (HLF-SNN) are designed to extract multi-level features from the SAR image. Based on this, a Spike-SARYOLO target detection and recognition network model is constructed, including a lightweight SAR image multi-level feature extraction backbone, a cross-scale feature fusion neck network, and a target detection head. Finally, the Spike-SARYOLO network model is trained using a training dataset and inferred using test set data to obtain target detection and recognition results. This invention significantly reduces energy consumption while maintaining high detection accuracy, providing an effective solution for the efficient application of low-cost artificial intelligence technology in the field of SAR image target detection and recognition.

This invention discloses the application of disodium 4,4'-diisothiocyanate-2,2'-stilbene sulfonate in the preparation of drugs for the prevention, delay, and / or treatment of blue light-induced retinal damage. It belongs to the field of biomedical technology. This invention demonstrates for the first time that sodium DIDS can effectively protect photoreceptor cells from blue light damage: at the cellular level, sodium DIDS can restore mitochondrial membrane potential, increase ATP levels, reduce reactive oxygen species accumulation, and inhibit mitochondrial-dependent apoptosis; at the animal level, intravitreal injection of sodium DIDS can improve vision in mice exposed to blue light, restore the amplitude of a-waves and / or b-waves in electroretinograms, and protect the outer segment length and outer nuclear layer thickness of cone cells. Sodium DIDS provides a novel therapeutic strategy for blue light-induced retinal photodamage through multi-target synergistic protection of mitochondrial function.

The invention discloses a method for measuring the concentration of sodium ions in a multi-mixing nickel electroplating bath solution based on a sodium ion selective electrode, relates to a method for measuring the content of sodium in an electroplating solution, and belongs to the technical field of solution analysis. The method aims at solving the technical problems that an existing common analysis method is complex in operation and low in accuracy. The method comprises the following steps: taking a standard sodium ion solution; recording the membrane potential E; drawing a standard curve; and measuring the sample. According to the method, the sodium ion concentration can be directly read based on an electrode method, the environment condition of a solution system is set, the sodium ion electrode is used for testing, the method is convenient, stable and rapid, and the testing range and accuracy of the method can meet analysis requirements. The method is used for measuring the concentration of the sodium ions in the multi-mixing nickel electroplating bath solution.