11 results about "Neuronal circuits" patented technology

The present invention discloses a neuron circuit and a brain-like computing system based on multi-neuronal morphological behavior. The neuron circuit maps the Hindmarsh-Ross model to the physical circuit field through a capacitor dynamic response mechanism and the cross-interval nonlinear characteristics of metal-oxide semiconductor field-effect transistors, and then implements the extended Hindmarsh-Ross model through a metal-oxide semiconductor field-effect transistor design. The neuron circuit includes a membrane voltage circuit, a slow variable circuit, and a recovery variable circuit. The membrane voltage circuit is used to receive an input signal and accumulate membrane voltage. The slow variable circuit is used to accumulate a slow variable according to the membrane voltage, and to cause the neuron circuit to enter a hyperpolarization stage after the slow variable is greater than a preset threshold. The recovery variable circuit is used to delay the output of a recovery variable according to the membrane voltage, and to reset the membrane voltage to a resting potential through the recovery variable.

Methods of treating one or more eating disorders including anorexia nervosa, bulimia, and related clinical syndromes in a subject in need thereof are described. In some cases, electric stimulation and / or one or more active agents are administered to a subject in need thereof to reduce or inhibit the activity of CeA PKC-δ neurons and ovBNST PKC-δ neurons in the brain of the subject. Preferably, the disclosed methods are effective to increase or excite the activity of the brain regions downstream of CeA PKC-δ neurons and ovBNST PKC-δ neurons including medial part of CeA and ventral lateral part of BNST.

The invention provides a neuron circuit. The neuron circuit comprises n input tubes, an integrating capacitor, an inverted output unit and a reset unit, the input tube is selectively gated based on an input voltage and a control voltage; the integrating capacitor accumulates charges and then releases the charges; the inverted output unit generates an output voltage; the reset unit resets the potential of the integrating capacitor. The invention further provides an activation method of the neuron circuit, and the neuron circuit is activated by controlling the integrating capacitor to accumulate charges and then discharge. The invention further provides a preparation method of the field effect transistor. The source electrode structure, the drain electrode structure and the grid electrode structure are formed on the provided substrate. The invention further provides a neural network which comprises an input layer and an output layer, and the output end of a single neuron circuit of the input layer is connected with the input ends of all or part of neuron circuits of the output layer. The signal processing system comprises an electric signal input device, a reading device and a neural network, the electric signal input device provides input data; the readout device reads the output data.

The BoC includes one or more chambers for accommodating neurons to be cultured, and one or more cell communication platforms placed between at least one pair of the one or more chambers. The cellular communication platform includes one or more features / structures configured, shaped, dimensioned and arranged to promote growth of axons of a population of neurons in a preferred direction and to impede growth of the axons in a direction opposite the preferred direction, thereby controlling at least one of directivity, transitivity, and / or lateral diffusion between the cultured population of neurons. The cellular communication platform establishes a neuron loop between the population of neurons in each pair of chambers.

The invention relates to the technical field of deep learning, and discloses a behavior monitoring and risk early warning system for obstetrical patients, which comprises a data sensing acquisition module, a data analysis processing module, a human body area modeling module, a behavior early warning module, an edge calculation module and a comprehensive judgment module. According to the system, pixel-level segmentation and key point regression are performed on patient image data by constructing a mask key point-continuous time attention fusion network, a continuous time attention mechanism inspired by a neuron loop is introduced, dynamic modeling and weight distribution are performed on continuous frame attitude features, and a continuous frame attitude fusion model is constructed. Therefore, the continuous time evolution trend of the posture change of the obstetrical patient is described. And in combination with human body area modeling and behavior early warning strategies, intelligent identification and early warning of risk behaviors such as postpartum dysphoria, abnormal turning over and falling down are realized. The accuracy and stability of behavior monitoring are improved, and the method is suitable for intelligent safety management in an obstetrical monitoring scene.

The invention discloses a nerve-imitating device with a three-dimensional structure and a manufacturing method of the nerve-imitating device. The disclosed nerve-imitating device with the three-dimensional structure can be a device with the three-dimensional structure capable of carrying out equilibrium propagation nerve-imitating operation, and the nerve-imitating device with the three-dimensional structure can be a device with the three-dimensional structure and capable of carrying out equilibrium propagation nerve-imitating operation. The nerve-imitating device may include: a plurality of synaptic blocks; a plurality of connection members for electrically connecting the plurality of synaptic blocks to each other; and a neuron circuit unit connected between the plurality of synaptic blocks. The plurality of synaptic blocks may each have a three-dimensional structure in which a plurality of vertical phase change memory cells are included. The vertical phase change memory cells may include: a first vertical electrode; a first vertical structure including a phase change material layer surrounding at least a portion of the first vertical electrode; and a plurality of first electrode layers in contact with the outer peripheral surface of the first vertical structure and spaced apart from each other in the vertical direction. The neuron circuit portion may include a bidirectional threshold switching (OTS) device, and a neuron circuit portion may include a bidirectional threshold switching (OTS) device.

The invention discloses a method for improving the synchronization capability of a neuron circuit by using nonvolatile memristor coupling. On the basis of an existing HR and tab-learning neuron model, a nonvolatile switchable memristor model is introduced to simulate synapses with long-term and short-term plasticity switchable, and a memristor coupling double-neuron model is established based on the synapses. Neuron synchronous control is realized by utilizing nonvolatile parameters of the memristor in a neuron coupling system for the first time, which is shown in that when the parameters of the memristor are volatile, the coupled neuron system needs relatively high coupling strength to realize synchronization, and when the parameters of the memristor are nonvolatile, the coupled neuron system needs relatively high coupling strength to realize synchronization. The coupling neuron system can realize synchronization under relatively small coupling strength.

The application discloses a LIF neuron circuit which can realize connection of multiple synapses based on delay. The application comprises a membrane potential accumulation circuit, a waveform shaping circuit, a leakage circuit, a pulse generation circuit, a refractory period circuit and a delay circuit. The LIF neuron with a memristor has the advantages of simple circuit structure, and can control the spike pulse width, peak strength, refractory period length of neuron excitation and delay connection of multiple synapses. The LIF neuron with a memristor not only can better match the function structure of a biological neuron, but also can transmit more abundant space-time information for multiple synapses in actual application, and enhance the adaptability of a neural network. Moreover, the LIF neuron circuit with a memristor and multiple synapses delay has fewer devices, and is favorable for improving the integration density of a brain-like chip.

A neuron circuit (100), including a memristive element (M1) used to receive an excitation signal; a trigger element (D1) connected to the memristive element (M1) and used to receive a clock control signal for the neuron circuit and an output signal of the memristive element (M1); a feedback element (T1) connected to an output end of the trigger element (D1) and an input end of the memristive element (M1), and used to control a voltage at the input end of the memristive element (M1); and an AND circuit (A1) used to perform an AND operation on an output signal of the trigger element (D1) and the clock control signal. An output signal of the AND circuit (A1) acts as an output signal of the neuron circuit (100).

The present application relates to a kind of random neuron circuit and its discharge mode extraction method, a kind of neuron circuit is constructed based on threshold transition device, resistance and capacitor, the pulse emission characteristics of neuron is realized based on the circuit.Compared with other neuron circuits based on threshold transition device, the neuron circuit can realize the conversion of different discharge modes under fixed parameters, and due to the randomness of neuron, the mode switching of circuit output has the probability transition behavior, which is beneficial to construct high-order intelligent brain-like system.At the same time, the present application uses the integral and resonance characteristics of H-H neuron itself to extract the mode of the probability output of the previous neuron, uses the resonance characteristics of unimodal detection neuron circuit to extract the unimodal output of intermediate neuron circuit, and uses the integral characteristics of bimodal detection neuron circuit to extract the bimodal output of intermediate neuron circuit, so as to complete the calculation process of multiplexing encoding and decoding of information.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for combined dynamic causal modeling and biophysical modeling of brain function are provided. In particular, the disclosed methods of brain function modeling can be used to integrate brain function measurements by two or more methods, such as functional neuroimaging and electrophysiological techniques. The present invention uses sequential model fitting to improve modeling accuracy to generate more comprehensive brain neuronal circuit models.