5 results about "Neuronal firing" patented technology

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.

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.

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.

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.

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.