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68 results about "Spike-timing-dependent plasticity" patented technology

Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of connections between neurons in the brain. The process adjusts the connection strengths based on the relative timing of a particular neuron's output and input action potentials (or spikes). The STDP process partially explains the activity-dependent development of nervous systems, especially with regard to long-term potentiation and long-term depression.

Producing spike-timing dependent plasticity in a neuromorphic network utilizing phase change synaptic devices

Embodiments of the invention relate to a neuromorphic network for producing spike-timing dependent plasticity. The neuromorphic network includes a plurality of electronic neurons and an interconnect circuit coupled for interconnecting the plurality of electronic neurons. The interconnect circuit includes plural synaptic devices for interconnecting the electronic neurons via axon paths, dendrite paths and membrane paths. Each synaptic device includes a variable state resistor and a transistor device with a gate terminal, a source terminal and a drain terminal, wherein the drain terminal is connected in series with a first terminal of the variable state resistor. The source terminal of the transistor device is connected to an axon path, the gate terminal of the transistor device is connected to a membrane path and a second terminal of the variable state resistor is connected to a dendrite path, such that each synaptic device is coupled between a first axon path and a first dendrite path, and between a first membrane path and said first dendrite path.
Owner:IBM CORP

Sensory input processing apparatus in a spiking neural network

Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.
Owner:BRAIN CORP

Solving the distal reward problem through linkage of stdp and dopamine signaling

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.
Owner:NEUROSCI RES FOUND

Elementary network description for efficient memory management in neuromorphic systems

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Methods for managing memory in a processing system are described whereby memory can be allocated among a plurality of elements and rules configured for each element such that the parallel execution of the spiking networks is most optimal.
Owner:QUALCOMM INC

Elementary network description for neuromorphic systems

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.
Owner:QUALCOMM INC

Elementary network description for efficient implementation of event-triggered plasticity rules in neuromorphic systems

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP.
Owner:QUALCOMM INC

Solving the distal reward problem through linkage of STDP and dopamine signaling

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.
Owner:NEUROSCI RES FOUND

Spiking neural network object recognition apparatus and methods

Apparatus and methods for feedback in a spiking neural network. In one approach, spiking neurons receive sensory stimulus and context signal that correspond to the same context. When the stimulus provides sufficient excitation, neurons generate response. Context connections are adjusted according to inverse spike-timing dependent plasticity. When the context signal precedes the post synaptic spike, context synaptic connections are depressed. Conversely, whenever the context signal follows the post synaptic spike, the connections are potentiated. The inverse STDP connection adjustment ensures precise control of feedback-induced firing, eliminates runaway positive feedback loops, enables self-stabilizing network operation. In another aspect of the invention, the connection adjustment methodology facilitates robust context switching when processing visual information. When a context (such an object) becomes intermittently absent, prior context connection potentiation enables firing for a period of time. If the object remains absent, the connection becomes depressed thereby preventing further firing.
Owner:BRAIN CORP

Producing spike-timing dependent plasticity in an ultra-dense synapse cross-bar array

ActiveUS20110153533A1Increasing and decreasing conductanceDigital computer detailsDigital dataSpike-timing-dependent plasticityUltra dense
Embodiments of the invention relate to producing spike-timing dependent plasticity in an ultra-dense synapse cross-bar array for neuromorphic systems. An aspect of the invention includes when an electronic neuron spikes, an alert pulse is sent from the spiking electronic neuron to each electronic neuron connected to the spiking electronic neuron. When the spiking electronic neuron sends the alert pulse, a gate pulse is sent from the spiking electronic neuron to each electronic neuron connected to the spiking electronic neuron. When each electronic neuron receives the alert pulse, a response pulse is sent from each electronic neuron receiving the alert pulse to the spiking electronic neuron. The response pulse is a function of time since a last spiking of the electronic neuron receiving the alert pulse. In addition, the combination of the gate pulse and response pulse is capable increasing or decreasing conductance of a variable state resistor.
Owner:IBM CORP

Spiking network apparatus and method with bimodal spike-timing dependent plasticity

ActiveUS20140229411A1Facilitate network normalizationPreventing feedback loopImage enhancementImage analysisNeuron networkBi modal
Apparatus and methods for learning in response to temporally-proximate features. In one implementation, an image processing apparatus utilizes bi-modal spike timing dependent plasticity in a spiking neuron network. Based on a response by the neuron to a frame of input, the bi-modal plasticity mechanism is used to depress synaptic connections delivering the present input frame and to potentiate synaptic connections delivering previous and / or subsequent frames of input. The depression of near-contemporaneous input prevents the creation of a positive feedback loop and provides a mechanism for network response normalization.
Owner:QUALCOMM INC

Stochastic synapse memory element with spike-timing dependent plasticity (STDP)

An active memory element is provided. One embodiment of the invention includes a bi-polar memory two-terminal element having polarity-dependent switching. A probability of switching of the bi-polar memory element between a first state and a second state decays exponentially based on time delay and a difference between received signals at the two terminals and a switching threshold magnitude.
Owner:IBM CORP

Elementary network description for efficient link between neuronal models and neuromorphic systems

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The format is specifically tuned for neural systems and specialized neuromorphic hardware, thereby serving as a bridge between developers of brain models and neuromorphic hardware manufactures.
Owner:QUALCOMM INC

Memristor-based neuron circuit

ActiveCN106845634ARealize integrated discharge functionImplement time encodingPhysical realisationInformation processingSynapse
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.
Owner:HUAZHONG UNIV OF SCI & TECH

Electronic Neuromorphic System, Synaptic Circuit With Resistive Switching Memory And Method Of Performing Spike-Timing Dependent Plasticity

A synaptic circuit performing spike-timing dependent plasticity STDP interposed between a pre-synaptic neuron and a post-synapse neuron includes a memristor having a variable resistance value configured to receive a first signal from the pre-synaptic neuron. The circuit has an intermediate unit connected in series with the memristor for receiving a second signal from the pre-synaptic neuron and provides an output signal to the post-synaptic neuron. The intermediate unit receives a retroaction signal generated from the post-synaptic neuron and the memristor modifies the resistance value based on a delay between two at least partially overlapped input pulses, a spike event of the first signal and a pulse of the retroaction signal, in order to induct a potentiated state STP or a depressed state STD at the memristor. An electronic neuromorphic system having synaptic circuits and a method of performing spike timing dependent plasticity STDP by a synaptic circuit are also provided.
Owner:POLITECNICO DI MILANO

Cortical neuromorphic network, system and method

A cortical neuromorphic network, system and method employ a plurality of neuromorphic nodes arranged in a network layer. The cortical neuromorphic network includes a neuromorphic node of the network layer in which the neuromorphic node includes a spike timing dependent plasticity (STDP) synapse and a neuromorphic neuron. The neuromorphic node is configured to receive a feedforward spike signal from selected ones of a plurality of input neurons of an input layer and to provide an output spike signal as a recurrent spike signal to the neuromorphic nodes of the network layer. A combination of the recurrent and feedforward spike signals is an excitatory spike signal of the neuromorphic node. The cortical neuromorphic system includes the neuromorphic nodes configured to operate according to a cycle and time slots of synaptic time multiplexing. The method includes receiving and weighting the excitatory spike signal using the STDP synapse and producing the output spike signal.
Owner:HRL LAB

Synapse for function cell of spike timing dependent plasticity (STDP), function cell of stdp, and neuromorphic circuit using function cell of stdp

A synapse for a spike timing dependent (STDP) function cell includes a memory device having a variable resistance, such as a memristor, and a transistor connected to the memory device. A channel of the memory device is connected in series with a channel of the transistor.
Owner:SAMSUNG ELECTRONICS CO LTD +1

Unit, device and method for simulating biological neuron and neuronal synapsis

The invention discloses a unit, a device and a method for simulating biological neuron and neuronal synapsis on the basis of chalcogenide compounds. The unit comprises a first electrode layer, a function material layer and a second electrode layer. During the neuron simulation, a device receives the stimulation of one or a plurality of electric pulses, the resistance of the function material is changed into the low resistance state from the high resistance state, the simulated neuron is changed into an excitation state from a resting state, and the threshold value excitation and energy accumulation excitation functions are realized. During the neuronal synapsis simulation, the electric conductance of the function material layer of the device can be gradually changed according to input signals, and the synapsis weight regulating function is realized, the ynapsis weight is changed according to time differences of signals input at two ends, and the STDP (spike timing dependent plasticity) function of synapsis is realized. The basic device forming the artificial neural network can be provided.
Owner:HUAZHONG UNIV OF SCI & TECH

Elementary network description for efficient memory management in neuromorphic systems

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Methods for managing memory in a processing system are described whereby memory can be allocated among a plurality of elements and rules configured for each element such that the parallel execution of the spiking networks is most optimal.
Owner:QUALCOMM INC

Synapse circuit and neuromorphic system including the same

A synapse circuit to perform spike timing dependent plasticity (STDP) operation is provided. The synapse circuit includes a memristor having a resistance value, a transistor connected to the memristor, and the transistor configured to receive at least two input signals. The resistance value of the memristor is changed based on a time difference between the at least two input signals received by the transistor.
Owner:SAMSUNG ELECTRONICS CO LTD +1

Spike timing dependent plasticity apparatus, system and method

A spike timing dependent plasticity (STDP) apparatus, neuromorphic synapse system and a method provide STDP processing of spike signals. The STDP apparatus includes a first leaky integrator to receive a first spike signal and a second leaky integrator to receive a second spike signal. An output of the first leaky integrator is gated according to the second spike signal to produce a first gated integrated signal and an output of the second leaky integrator is gated according to the first spike signal to produce a second gated integrated signal. The STDP apparatus further includes an output integrator to integrate a difference of the first and second gated integrated signals to produce a weighted signal. The system includes a synapse core and the STDP apparatus. The method includes integrating the spike signals, gating the integrated signals and integrating a difference of the gated integrated signals.
Owner:HRL LAB

Impulsive neural network-based image feature describing and memorizing method

The invention provides an impulsive neural network-based image feature describing and memorizing method. the method comprises steps: M normalized images are inputted, the layer number of the impulsive neural network is determined according to the size of the image, a gradient direction at each pixel point is acquired when pretreatment is carried out on the images, the gradient direction is discretized into a preset individual value, distribution of one of each preset value number of neurons in the first layer in the impulsive neural network is determined according to the discretized gradient direction, membrane potential of neurons in the second layer and the distribution condition of the neurons in the second layer are calculated according to the distribution condition of the neurons in the first layer, the distribution conditions of the neurons in all layers are obtained, a connection weight of each layer of the impulsive neural network is adjusted according to a timing relationship for distribution of neurons in all layers and a STDP (Spike Timing-dependent Plasticity) rule, and the image features are described and memorized in a connection weight form. The method of the invention can describe and memorize images of various kinds, can completely restore an image, and also has an image classification function.
Owner:TSINGHUA UNIV

Systems and methods for providing a neural network having an elementary network description for efficient implementation of event-triggered plasticity rules

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. The software and hardware engines are optimized to take into account short-term and long-term synaptic plasticity in the form of LTD, LTP, and STDP.
Owner:QUALCOMM INC

Weight adjustment circuit for variable-resistance synapses

InactiveCN102610274AImplement STDP weight adjustment functionSimple structureDigital storageSynapseNerve network
The invention discloses a weight adjustment circuit for variable-resistance synapses, which relates to the fields of integrated circuits and neural networks, and is used for carrying out weight adjustment on variable-resistance synapses. The circuit is composed of a weight enhancement adjustment subcircuit A (LTP (long term potentiation) adjustment) and a weight inhibition adjustment subcircuit B (LTD (long term depression) adjustment), wherein the two subcircuits respectively contain a charging pole, a discharging pole, a charge storage pole and an output pole. The core of the circuit is implemented by using an analog circuit mode, therefore, the number of transistors required by the circuit is greatly reduced; and meanwhile, through the setting of the bias voltage on a discharge tube in the discharge pole, the size of a weight adjustment time window can be adjusted conveniently. The circuit disclosed by the invention follows an STDP (spike timing dependent plasticity) learning rule, and LTP and LTD pulse outputs are generated according to the activities of nerve units at the two ends of the variable-resistance synapses so as to carry out corresponding weight adjustment on the variable-resistance synapses. The circuit disclosed by the invention is simple in structure, convenient in parameter adjustment, and suitable for applications, such as weight adjustment on electronic synapses of a large-scale neural network, and the like.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Spike timing activity conversion circuit

The invention relates to a principle about the connection among neurons in neural networks in integrated circuits and biology. A spike timing activity conversion circuit is composed of spike timing activity conversion logic and a control switch. The spike timing activity conversion logic receives a signal (PRE) and a signal (POST) which are treated to generate a long term potentiation (LTP) signal, a long term depression (LTD) signal or a transmission control (TR) signal. The three signals are output to the control switch, which is in connection with write operation voltage (Vw) and read operation voltage (Vr) of a resistive synapse. Under the control of the TR, LTP and LTD signals, output (OUT) of transinformation can be generated, or proper voltage can be output to Vw1 and Vw2 which are voltages respectively added to the two ends of the resistive synapse. The invention provides a conversion circuit, which can convert a spike timing activity relation into corresponding LTP and LTD spikes for output according to an STDP (spike timing dependent plasticity) rule, thus realizing the function of an STDP model. Therefore, the conversion circuit can be conveniently applied in neural networks, electronic synaptic weight adjustment and other circuits.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Neuron peripheral circuits for neuromorphic synaptic memory array based on neuron models

A neuromorphic memory system including neuromorphic memory arrays. Each neuromorphic memory array includes rows and columns of neuromorphic memory cells. A column of postsynaptic circuits is electrically coupled to postsynaptic spike timing dependent plasticity (STDP) lines. Each postsynaptic STDP line is coupled to a row of neuromorphic memory cells. A column of summing circuits is electrically coupled to postsynaptic leaky integrate and fire (LIF) lines. Each postsynaptic LIF line is coupled to the row of neuromorphic memory cells at a respective memory array. Each summing circuit provides a sum of signals from the postsynaptic LIF lines to a respective postsynaptic circuit.
Owner:SAMSUNG ELECTRONICS CO LTD
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