46 results about "Presynaptic neuron" 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 a neural network information conversion method and system. The method comprises the steps that neuron input information input by a presynaptic neuron is received, wherein artificial neuron input information input by a presynaptic artificial neuron or spiking neuron input information input by the presynaptic spiking neuron is included; according to the artificial neuron input information input by the presynaptic artificial neuron, through a preset artificial information conversion algorithm, the artificial neuron input information is converted to spiking neuron conversion information; or according to the spiking neuron input information, through a preset spiking information conversion algorithm, the spiking neuron input information is converted to artificial neuron conversion information; the spiking neuron conversion information or the artificial neuron conversion information is output. According to the neural network information conversion method and system, in one neural network, two different neuron information modes are compatible, and the information processing ability of the neural network is improved.

A neuron circuit performing synapse learning on weight values includes a first sub-circuit, a second sub-circuit, and a third sub-circuit. The first sub-circuit is configured to receive an input signal from a pre-synaptic neuron circuit and determine whether the received input signal is an active signal having an active synapse value. The second sub-circuit is configured to compare a first cumulative reception counter of active input signals with a learning threshold value based on results of the determination. The third sub-circuit is configured to perform a potentiating learning process based on a first probability value to set a synaptic weight value of at least one previously received input signal to an active value, upon the first cumulative reception counter reaching the learning threshold value, and perform a depressing learning process based on a second probability value to set each of the synaptic weight values to an inactive value.

A neuromorphic device is provided. The neuromorphic device may include a pre-synaptic neuron, a row line extending from the pre-synaptic neuron in a row direction, a post-synaptic neuron, a column line extending from the post-synaptic neuron in a column direction, and a synapse at an intersection region between the row line and the column line. The synapse may include a switching device and a memristor electrically connected with each other in series. The post-synaptic neuron may include a summation circuit, a variable resistor, and a comparator.

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.

The invention relates to a calculation method of neural synaptic plasticity based on calcium concentration, which relates to the field of brain simulation, in particular to the calculation problem ofneural synaptic plasticity of impulse neural network in brain simulation. The first is the calculation of calcium ion concentration. According to the membrane potential values of presynaptic neurons and postsynaptic neurons at the initial time t0 and the initial connecting weight w0 of synapses, the calcium ion concentrations in dendrites and spines at the next time t1 are calculated respectivelyfor the synapses that need to be calculated. Secondly, according to the calcium ion concentration in dendritic spine, the direction of weight change was obtained by comparing with the calcium ion concentration threshold Ca0s and Ca1s. According to the synaptic state tag Tag and the plasticity related protein PRP concentration, the change of synaptic weight was calculated, and the new weight at time t1 was obtained. the above procedure is repeated to calculate the strength of synaptic connections within the simulation time. The method of the invention is applied to constructing a brain-like neural network, completing the simulation of the learning and memory process required by the brain-like intelligence, realizing the universal strong artificial intelligence, and is applied to intelligentmedia, medical treatment and the like.

According to exemplary embodiments of the present disclosure, it is possible to provide method, system, arrangement, computer-accessible medium and device to stimulate individual neurons in brain slices in any arbitrary spatio-temporal pattern, using two-photon uncaging of photo-sensitive compounds such as MNI-glutamate and / or RuBi-Glutamate with beam multiplexing. Such exemplary method and device can have single-cell and three-dimensional precision. For example, by sequentially stimulating up to a thousand potential presynaptic neurons, it is possible to generate detailed functional maps of inputs to a cell. In addition, it is possible to combine this exemplary approach with two-photon calcium imaging in an all-optical method to image and manipulate circuit activity. Further exemplary embodiments of the present disclosure can include a light-weight, compact portable device providing for uses in a wide variety of applications.

A neuromorphic device may include: a plurality of pre-synaptic neurons; row lines extending in a row direction from the plurality of pre-synaptic neurons; a plurality of post-synaptic neurons; column lines extended in a column direction from the plurality of post-synaptic neurons; a plurality of synapses arranged at intersections between the row lines and the column lines; a plurality of first control blocks; and first control lines extending from the control blocks. The first control lines may be electrically connected to the plurality of synapses.

An event-driven neural network includes a plurality of interconnected core circuits is provided. Each core circuit includes an electronic synapse array has multiple digital synapses interconnecting a plurality of digital electronic neurons. A synapse interconnects an axon of a pre-synaptic neuron with a dendrite of a post-synaptic neuron. A neuron integrates input spikes and generates a spike event in response to the integrated input spikes exceeding a threshold. Each core circuit also has a scheduler that receives a spike event and delivers the spike event to a selected axon in the synapse array based on a schedule for deterministic event delivery.

An electronic neuronal circuit system to model the interaction between the postsynaptic terminal of a first synapse between two neurons and the postsynaptic terminal of a second synapse between two neurons includes comparators to model the presynaptic neurons of the synapses, plurality of three diodes connected to the comparators to model synapses, an AND gate and latch to model the formation of functional link between the postsynaptic terminals, and timer-controlled latches for controlling the life-span of the inter-postsynaptic functional link, durations of re-activation of inter-postsynaptic functional link and flow of activity through the output postsynaptic dendritic terminals.