32 results about "Neuronal firing" patented technology

In Pavlovian and instrumental conditioning, rewards typically come seconds after reward-triggering actions, creating an explanatory conundrum known as the distal reward problem or the credit assignment problem. How does the brain know what firing patterns of what neurons are responsible for the reward if (1) the firing patterns are no longer there when the reward arrives and (2) most neurons and synapses are active during the waiting period to the reward? A model network and computer simulation of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA) is disclosed to answer this question. STDP is triggered by nearly-coincident firing patterns of a presynaptic neuron and a postsynaptic neuron on a millisecond time scale, with slow kinetics of subsequent synaptic plasticity being sensitive to changes in the extracellular dopamine DA concentration during the critical period of a few seconds after the nearly-coincident firing patterns. Random neuronal firings during the waiting period leading to the reward do not affect STDP, and hence make the neural network insensitive to this ongoing random firing activity. The importance of precise firing patterns in brain dynamics and the use of a global diffusive reinforcement signal in the form of extracellular dopamine DA can selectively influence the right synapses at the right time.

The invention relates to the use of cannabidiol (CBD), at a dose of greater than 300 mg / day, in combination with a standard anti-epileptic drug (SAED) which acts via sodium or calcium channels, for use in the treatment of epilepsy. The SAED is preferably one which•modifies low-threshold or transient neuronal calcium currents,or•reduces high-frequency neuronal firing and sodium-dependent action potentials and enhances GABA effects. Preferred SAEDs are ethosuximide and valproate.

A method and system are provided for updating a neuron membrane potential in a spike time dependent plasticity model in a Neuromorphic system. The method includes approximating a shape of an analog spike signal from an axon input using a hardware-based digital axon timer. The method further includes generating a first intermediately updated neuron membrane potential value from a current axon timer value, a current synapse weight value and a current neuron membrane potential value using a first look-up table and an accumulator. The method also includes generating a second intermediately updated neuron membrane potential value with a leak decay effect using a second look-up table and the first intermediately updated neuron membrane potential value. The method additionally includes generating a final updated neuron membrane potential value based on a comparison of the second intermediately updated neuron membrane potential value with a neuron fire threshold level using a comparator.

The invention discloses a memristor-based neuron circuit. According to the neuron circuit, partially volatile bipolar resistive transition devices are selected and used as memristors of a synapse array, and a volatile resistive transition device is selected and used as a memristor for expressing membrane potential of neurons, so that the neuron circuit is formed; and synapse basis units are arranged. The neuron circuit can realize an integral discharge function in biological neurons and express local graded potentials; and synapses have partial volatility, can express spike-timing-dependent plasticity, and have great similarity with biological neurons and synapses in the aspects of information storage, transmission and processing. According to the neuron circuit, the basic units can be provided for hardware to simulate a cerebral neural network structure; the technical problems of neuron discharge time delay, difficulty in realizing high-density integration and the like in the prior art are solved; and the neuron circuit can be used for constructing a brain-like information processing system, can quickly process a large amount of information in parallel, and has a greatly high application value in realizing a cerebral neurologic calculation network.

The present application relates to a method, a device, a computer device and a storage medium for picking out neuron discharge spike signals. The method includes: preprocessing the discharge signal of the neuron to be picked to obtain a continuous filtered discharge signal; performing energy calculation on the continuously filtered discharge signal to obtain a continuous energy signal; performing peak statistics on the continuous energy signal to determine the energy peak value; Establish a cumulative distribution function of chi-square distribution according to the continuous energy signal for analysis to determine the picking threshold; filter the energy peak through the preliminary peak extraction model constructed by the picking threshold to obtain a preliminary peak; Extracting signal segments from the continuously filtered discharge signal to obtain a discharge signal segment; analyzing the discharge signal segment according to the picking threshold to determine a neuron discharge spike signal. Through this method, the robustness of picking neuron firing spikes is stronger, and the accuracy of picking results is also improved.

The invention discloses a magnetic resonance imaging method used for oscillatory magnetic field detection and based on a spin-locked technology and application, and names as Spin-Locked Oscillatory Excitation, SLOE. SLOE comprises a spin-locked preparation sequence insensitive to main magnetic field and radio frequency field inhomogeneity and spin planar echo acquisition. According to the method, after the spin-locked preparation sequence, any additional excitation pulse is no longer applied, and all acquired signals are magnetization vector signals deflected by oscillatory magnetic field excitation. In a data processing process, due to the fact that the initial phase of oscillatory current scanned every time is random, the fluctuation of a check signal time sequence is statistically used to detect an activation signal. According to the invention, defects of conventional neuronal discharge sequence detection are solved; the sensitivity of tiny oscillatory magnetic field detection is greatly improved; neuronal discharge can be successfully detected through magnetic resonance detection in vivo; and technological innovation in the field can be promoted.

The invention provides a neuron phase response characteristic measuring system based on a discharge rate clamp closed loop. The system comprises a neuron discharge rate detection device, a PID control device and a pulse generation device which are mutually connected. The neuron phase response characteristic measuring system based on the discharge rate clamp closed loop has the advantages that according to the measuring system, by means of closed-loop control, the neuron discharge rate is maintained, the advantage of closed-loop electrophysiology is effectively utilized, measurement of multiple groups of phase response curves can be conveniently conducted by changing the discharge rate value, the accuracy of pulse stimulation time and phase positions is achieved, and important value is achieved for phase response experimental study.

Certain aspects of the present disclosure support techniques for modulating neuronal firing via noise control. The response curve of a typical neuron with a threshold can transition from never firing to always firing with very small changes in the neuron's input, thereby limiting the range of excitable input patterns for the neuron. The slope of the neuron response curve can be reduced by introducing local, regional and global noise terms. This may enable a larger set of input spike patterns to be effective in firing the neuron, that is, enable the neuron to respond to a large range of input patterns rather than the inherently smaller set of patterns in the absence of noise.

The invention relates to a method for constructing a spatiotemporal network of a multi-electrode array neuron discharge sequence, comprising the following steps: a first step, dividing a time window for each neuron discharge time sequence; a second step, constructing a spatiotemporal network; and a third step, a network Feature analysis: The structural parameters of the multi-neuron spatio-temporal network, including the number of connected neurons D in each time window, the number of interconnected neuron clusters M, the time length of cluster connection existence T, and the width of cluster connection existence W.

The invention discloses an image segmentation system and method based on Spiking-SOM neural network clustering. Firstly, the median filtering method is adopted to denoise the target image; Window, calculate the RGB average value of all pixels in the superpixel as the color feature of the superpixel; then select K IF neurons to construct the Spiking-SOM neural network, and construct the initial weight of the network based on the distance between the color features calculated between the superpixels Value matrix, and use the Hebbian rule to train the network. After the network training is over, clustering is performed according to the synchronization and asynchrony of neuron discharge; finally, the RGB average value of the same superpixel is calculated, and it is used to replace the original superpixel RGB value, and reset After the image matrix, the image segmentation result is obtained. The invention combines the advantages of segmentation speed and segmentation accuracy, can effectively segment color images in natural scenes, and has certain potential application value and advancement.

The invention relates to a method for detecting weak edges of images on the basis of discharge information of multilayer neuron groups. The method includes constructing the multilayer neuron groups with interconnected inhibitory synapses, inputting digital images into input-layer neuron groups and representing image pixels by the time-space information of first discharge of various neurons; describing space details of the images by time variances by the aid of visual receptive fields and discharge time sequences of the various neurons, selecting attentive mechanisms in the consideration of lateral inhibition so as to acquire visual attention data of information of the images; implementing space variable-resolution mechanisms on the basis of a combination of selective attention procedures by the aid of Log-Gabor multi-direction filter results, acquiring reconstructed information of the edges of the images and reinforcing the information of the edges of the images by the aid of output-layer neuron groups. The method has the advantages that a synapse interconnection characteristic of the neuron groups is taken into consideration; simple visual information procedures of cortices are reflected by the aid of multi-direction filter mechanisms; the weak edges of the images can be effectively detected by the aid of the multilayer neuron groups.