7 results about "Presynaptic neuron" patented technology

This invention relates to the field of neuromorphic computing technology and provides a method and system for dynamic adjustment of synaptic weights in real-time neurofeedback for neuromorphic chips. The method includes: capturing the pulse signals and timestamps emitted by presynaptic and postsynaptic neurons; calculating the time difference between the presynaptic and postsynaptic pulses; when the absolute value of the time difference is less than a preset time window threshold, querying a pulse timing dependency plasticity rule base based on the sign of the time difference to determine the corresponding synaptic weight adjustment type; generating corresponding voltage pulse parameters based on the adjustment type and the current conductance state of the target memristor synapse; and applying a write voltage pulse to the target memristor synapse according to the voltage pulse parameters to adjust its conductance value in situ in real time, thereby dynamically updating the synaptic weights. This invention solves the problems of poor dynamic environment adaptability, low energy efficiency, and high learning latency caused by traditional offline weight update mechanisms.

The application provides a brain-like synapse learning method and a brain-like technology neural morphological hardware system, and the method comprises the following steps: determining a pulse pair generated by a presynaptic neuron and a postsynaptic neuron in a brain-like synapse learning circuit, wherein the pulse pair comprises an input pulse generated by the presynaptic neuron and an output pulse generated by the postsynaptic neuron; determining an STDP mechanism corresponding to the pulse pair and a synapse weight corresponding to the STDP mechanism based on the pulse pair; and performing STDP learning corresponding to the brain-like synapse learning circuit based on the pulse pair and the synapse weight; wherein the STDP mechanism is a pulse time-dependent plasticity mechanism, and the front and rear pulses of the pulse pair correspond to a long-term potentiation process or a long-term depression process in the STDP mechanism according to the time sequence. The application realizes online learning of brain-like intelligence, and plays the environment self-adaptive characteristics of brain-like computing.

The application discloses a hybrid integrated photon pulse neural network device, comprising: an optical coding module comprising N presynaptic neurons; an optical response output module comprising N postsynaptic neurons; the neurons are VCSEL-SAs, and N is greater than or equal to 1; an optical synapse module comprising a first MZI subnetwork, an MZI array and a second MZI subnetwork in sequence, forming an MZI network with N inputs and N outputs; the first MZI subnetwork and the second MZI subnetwork each comprise N(N-1) / 2 MZIs arranged in a Reck architecture, and the MZI array comprises N MZIs arranged in parallel; the phase parameters of the MZIs in the optical synapse module are obtained by singular value decomposition of an N*N weight matrix, construction of an equation group containing trigonometric functions based on a unitary matrix obtained by decomposition and solution of the equation group.

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.

A neuromorphic device includes: a neuron block unit including a plurality of neurons; a synapse block unit including a plurality of synapses; and a topology block unit including a plurality of parallel Look-Up Table (LUT) modules including pre and post neuron elements configured with addresses of a presynaptic neuron and a postsynaptic neuron. Each of the plurality of neurons has an intrinsic address, each of the plurality of synapses has an intrinsic address. The parallel LUT module is partitioned based on a first synapse address among synapse addresses, and each of the partitions is indexed based on a second synapse address among the synapse addresses.

The invention discloses a memristor neural network circuit based on emotion influence STDP learning rate, which realizes biological synaptic plasticity through resistance plasticity and threshold value characteristics of a memristor, and simulates the regulation effect of emotion on brain learning rate. The circuit is composed of a pre-synaptic neuron, a post-synaptic neuron, a pulse module and a sensitive module. Wherein the pre-synaptic neurons and the post-synaptic neurons receive environment input signals and emotion signals, and the input mood signals are learned by using the weight adjustment module and the memristor; the pulse module compares a difference value between the neuron output signal and the emotion signal, and outputs a pulse signal when the difference value is lower than a threshold voltage; the sensitive module dynamically adjusts the resistance value of the memristor according to the pulse signal so as to simulate the emotion, thereby adjusting the weight and adjusting the voltage. When the emotion is excited, the learning rate is increased; when the emotion is low, the learning rate is reduced, and the circuit effectively simulates the influence of the emotion on the STDP learning rate.

The invention relates to the technical field of neural networks, and discloses a spiking neural network acceleration method and device, equipment, a storage medium and a product, and the method comprises the steps: carrying out the descending sorting of pre-synaptic neurons according to the weight; gradually accumulating the weights of the activated pre-synaptic neurons into the membrane potential of the post-synaptic neurons according to the sorting sequence; in the step-by-step accumulation process, when the membrane potential of the post-synaptic neurons is larger than the excitation threshold potential, the post-synaptic neurons output pulses so as to improve the calculation efficiency.