16 results about "Neuronal firing" patented technology

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.

Embodiments of the present invention related to a neuromimetic circuit including a transmitter circuit to receive the threshold signal from a superconducting optoelectronic neuron and convert the small current pulse to a voltage pulse sufficient to produce light from a semiconductor diode. This light is the signal used to communicate between neurons in the network. The transmitter circuit in accordance with the present invention includes an amplifier chain that comprises two Josephson junctions, a superconducting thin-film current-gated current amplifier, and a superconducting thin-film current-gated voltage amplifier. The transmitter circuit in accordance with the present invention enable an amplification sequence that allows neuronal firing of about 20 MHz with power density sufficiently low to be cooled with standard 4He cryogenic systems operating at 4.2 K.

The invention relates to the technical field of biomedical engineering, in particular to an invasive mouse electroencephalogram data acquisition and analysis system and method. The system comprises a motion structure assembly used for providing a motion carrier for a mouse and collecting motion parameters; the control assembly is used for adjusting motor output according to the target parameter, generating a synchronous time label instruction, recording the actual motion parameter in real time and storing the actual motion parameter in association with the time label; and the data acquisition and analysis component is used for acquiring original electroencephalogram data, synchronously storing a time label, aligning an electroencephalogram sampling time sequence with a motion control time label sequence, extracting electroencephalogram pulse position information, calculating a neuron discharge frequency and establishing a correlation between the discharge frequency and a motion parameter. According to the technical scheme, accurate and synchronous acquisition, automatic alignment of time stamps and multi-dimensional correlation analysis of mouse motion states and electroencephalogram signals can be realized.

The invention discloses an equipment repair method and device, electronic equipment and a storage medium, and belongs to the field of automatic equipment repair. The method comprises the following steps: determining a multi-mode signal as a first input current of a neuron of a spiking neural network; determining a trigger threshold value through the operation environment parameter, and determining the trigger threshold value as a second input current of the neuron; according to the first input current and the second input current, determining an abnormal critical value for avoiding discharge of the neurons in real time, and determining the abnormal critical value determined in real time as a dynamic threshold value; and when the actually measured parameter of the multi-modal signal is greater than the dynamic threshold value, executing a preset repair strategy for the to-be-repaired equipment, thereby realizing real-time and self-adaptive adjustment of a fault early warning standard by fusing the multi-modal signal of the equipment and the operating environment parameter, thoroughly solving the problems of false alarm and missing alarm caused by a traditional fixed threshold value, and improving the fault early warning efficiency. And therefore, the self-repairing can be started at a more accurate opportunity.

This invention relates to a method for highlighting spike discharge data of intracranial electroencephalogram (EEG) signals. The method involves collecting background noise data from the patient's brain without neuronal discharges, preprocessing the background noise data, automatically selecting the optimal order of an autoregressive (AR) model using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), estimating the AR model coefficients using the Yul-Walker equation, and constructing a background noise model. The intracranial EEG signals to be processed are then subjected to high-pass filtering. A short-time Fourier transform (STFT) and window-based frame-by-frame processing strategy are used to subtract the spectrum of the signal from the noise. By adjusting the parameters of the spectral subtraction, noise removal and preservation of neuronal signal features are achieved.

The invention discloses a biological heuristic navigation control method and system, and relates to the technical field of automatic driving and robot control, and the method comprises the following steps: extracting environment features by using multi-source data fusion and semantic segmentation, and constructing a topological map containing semantic information through a graph neural network; then, a bionic model containing reward signals, local loops and value coding neurons is established in graph nodes of the topological map; wherein a memory integral term with an exponential decay kernel function is introduced into the local loop and the value coding neuron so as to integrate the historical state. During navigation, a reward signal is reversely propagated from a navigation target point along the topological map, and a reward signal neuron is activated; and monitoring value coding neuron discharge rates of neighbor graph nodes in real time, selecting the graph node with the highest discharge rate as a next moment target according to a greedy algorithm strategy, and dynamically planning a navigation path. According to the invention, autonomous navigation in a non-signal or weak-signal environment is realized, and decision robustness and data security are improved.

This invention discloses a method for denoising EEG data based on neuronal firing correlation, comprising: (1) acquiring EEG data and obtaining a denoised training set after preprocessing; (2) designing a multi-branch neural network model based on contrastive learning, using a multi-branch attention mechanism network to learn the correlation patterns between different neurons, calculating the correlation noise vector, and then subtracting the noise vector from the EEG data to obtain the denoised EEG data; (3) training the model using the denoised training set, adding constraints on neuronal firing correlation during the training process; calculating the intra-class and inter-class correlations of neurons in the denoised data and adding them to the overall loss function to be optimized with certain weights; (4) inputting the EEG data into the trained model to obtain the denoised EEG data. This invention can alleviate the inhibitory effect of correlation noise on the information content of EEG data to a certain extent, and effectively improve the quality and decoding effect of EEG data.

The invention discloses a method for realizing pattern recognition based on a bistable memristor neuron circuit. A bistable memristor neuron circuit is constructed through a bistable memristor, and under the driving of pulse voltage, the bistable memristor neuron circuit transitions from one discharge behavior to another discharge behavior, so that a neuron discharge behavior with bistability is realized. A transistor-memristor neural network mode identification scheme taking 1T1M as an example is successfully constructed by combining a threshold effect of steady-state switching of a discharge behavior of a memristor neuron circuit. The technical scheme has a wide application prospect in the consumer electronics fields such as voice recognition and image recognition functions in smart home equipment, and is expected to provide a new thought for developing more efficient neuromorphic calculation.

The present application relates to the technical field of working face mine pressure multi-parameter intelligent monitoring and early warning system, and specifically 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 module and an early warning information output terminal. The multi-source heterogeneous data such as hydraulic support resistance, microseismic energy and acoustic emission frequency are converted into pulse event sequences of biological neural signals, and the bionic model of simulated neuron discharge mechanism and synaptic plasticity is used to identify rock mass instability precursors. In combination with the dynamic attention mechanism, the key signals are adaptively strengthened and the noise is suppressed. The present application can capture multi-parameter coupling characteristics with high sensitivity, improve the early warning accuracy and advance, reduce the false alarm rate, and provide reliable protection for deep coal mine safety.

This invention relates to the fields of basic research in cognitive neuroscience, large-scale artificial intelligence model architecture, brain-computer interfaces, and educational cognitive assessment. Specifically, it relates to a unified four-dimensional discrete-time spatiotemporal modeling method for basic neural information elements of the brain (neural impulses, neuronal cluster firing, and sensory neural signals). This method is based on a homologous and unified underlying theoretical framework and is an engineering embodiment of a unified four-dimensional discrete-time spatiotemporal cognitive system. It shares a completely homologous, isomorphic, and unified mathematical framework with the inventor's previously submitted "A Token Tagging Method and Cognitive System for Input Data of a Large Language Model." The only difference is that the AI ​​patent processes computational information elements (Tokens), while this patent processes biological information elements (neural impulses, sensory signals, and neuronal firing events). Together, they constitute a unified underlying architecture covering biological cognition and artificial intelligence.

The present disclosure provides a neuron firing control method, a many-core system, a processing core and a medium. The method is applied to a synchronous neuron in a processing core, and the method comprises: in response to first firing information sent by a predecessor neuron of the synchronous neuron, performing a firing operation to obtain parameter state information of the synchronous neuron; and synchronously firing the synchronous neuron based on the parameter state information. According to the embodiment of the present disclosure, the transmission delay can be reduced, and the processing efficiency of the many-core system can be improved.

1. Name of the product in this design: Graphical User Interface for Electromyographic Neuron Decomposition of Electronic Devices. 2. Purpose of this design: To display interface content. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Electronic devices are designed in a conventional way, so other views are omitted. 6. Purpose of the graphical user interface: A graphical user interface for the decomposition of electromyographic neurons. Human-computer interaction methods: Human-computer interaction can be achieved through mouse dragging, sliding, placing, and clicking. The main view is the main interface of the electromyography neuron decomposition operation interface. Figure 1 shows the neuron decomposition interface displayed after clicking the MU option in the main view. Clicking "Run" in Figure 1 leads to the neuron firing rate calculation result interface, as shown in Figure 2. Checking the "Show Histogram" option in the pop-up window in Figure 2 displays the histogram interface, as shown in Figure 3. Figure 4 shows the histogram interface displayed after selecting the target data in Figure 3. Clicking the "MuClean" option in Figure 4 leads to the force load analysis interface, as shown in Figure 5. Clicking the "Analysis" option in Figure 5 leads to the calculation result interface, as shown in Figure 6. Clicking "Export" in Figure 6 leads to the export result interface, as shown in Figure 7.

The present application relates to a kind of novel Hedgehog signal pathway inhibitor and preparation method thereof, the Hedgehog signal pathway inhibitor has the structure shown in formula (I).The Hedgehog signal pathway inhibitor of the present application can effectively inhibit SMO receptor activity, and block SHH signal pathway. With good effect of blocking the seizure caused by excessive or super-synchronous neuron discharge in brain. As epilepsy treatment candidate drug has good application prospect.

This invention discloses a passive running device for a mouse with its head fixed, comprising a base plate, a support assembly, a head fixation component, and a running mechanism. The support assembly is disposed on the base plate. The head fixation component is used to fix the head of the mouse and is detachably connected to the support assembly. The running mechanism is disposed on the base plate and includes a driver and a running belt assembly. The head fixation component is located above the running belt assembly to restrain the mouse on the running belt assembly. The driver is driven by the running belt assembly to drive the running belt assembly, enabling the mouse on the running belt assembly to passively perform running movements. This invention, by fixing the head of the mouse and utilizing the movement of the running belt assembly to passively induce running movements, can assist in observing the two-photon neuronal electrical signals of the mouse during running using a two-photon microscopy system, and observe the cerebellar neuronal firing patterns during movement, thus contributing to neuroscience research.

The invention discloses a chaotic system modeling and predicting method based on a graph neural network symbol regression algorithm, which comprises the following steps of: constructing a symbol expression tree graph structure, expressing a differential equation to be discovered as a binary tree structure, and performing graphical processing on the tree structure to form a symbol expression tree graph with a self-loop; performing feature propagation on the symbol expression tree graph based on a multilayer graph neural network, predicting symbol types node by node according to a preorder traversal sequence, and generating a legal mathematical expression; a graph neural network optimization strategy is introduced, including exploration reward and similarity weighting strategy gradient, and training optimization is carried out on the sampling process of the graph neural network; and taking the reduced differential equation as physical priori knowledge, and embedding the reduced differential equation into a loss function of a physical information neural network to realize modeling and prediction of the state of the chaotic system. The method is more efficient and anti-noise, realizes a complete process from equation discovery to prediction, and can be widely applied to the fields of atmospheric flow, turbulence, neuron discharge, laser dynamics and the like.

The present disclosure relates to vehicle control based on a neural network. In particular, the present disclosure relates to determining confidence in the output of a neural network by comparing a firing pattern observed during operation of a vehicle with a reference firing pattern obtained by observing firing of neurons during a training phase.