Abstract: A calculation device converts, for each input signal to a spiking neuron model, the input time of the input signal, into a discrete-time input value, which is a value at a discretized time, calculates the membrane potential of the spiking neuron model at the discretized time, based on the discrete-time input value, and calculates the firing time of the spiking neuron model, based on the calculated membrane potential.

Abstract: A computing device includes: a multilayer spiking neural network including a plurality of neurons. and includes an additive computing portion in which a lower limit of membrane potential of the neurons in each layer is suppressed by learning.

Abstract: A neural network system includes a time-based spiking neural network configured using a neuron model based on an integrate-and-fire model, and trains the spiking neural network using an evaluation function that indicates a better evaluation as a probability that an individual neuron model fires decreases.

Abstract: A computing device calculates a product between an interconnectivity-representation matrix including a plurality of elements having values each set to 1, 0, or ?1 and a vector representing values of intermediate nodes, carries out a shift operation with a bit string in binary notation for each element among a plurality of elements of a vector obtained by the product, makes summation of a vector obtained by the shift operation and a vector including weighted input values, applies a function, which is determined as an activation function, for each element among a plurality of elements of a vector obtained by the summation of the vector obtained by the shift operation and the vector having the weighted input values, thus calculating a vector representing the values of the intermediate nodes updated in timestep progression, and calculates a plurality of output values by weighting the updated values of the intermediate nodes.

Abstract: A computing device includes: a neuron model in which an input time interval for receiving a spike and an output time interval for firing and a transmitting spike are divided, wherein the neuron model fires within the output time interval, with firing within the input time interval being restricted.

Abstract: A design method includes determining at least one of a firing time increment of a spike generator, the minimum increment of a current value output by a synapse circuit, a firing threshold voltage of the spike generator, the capacitance of a capacitor that simulates a membrane potential, the firing time increment of a mathematical model, and the weighting increment of the mathematical model such that the product of the firing time increment of the mathematical model and the weighting increment of the mathematical model is equal to a value obtained by dividing the product of the firing time increment of the spike generator and the minimum increment of the current value output by the synapse circuit by the product of the firing threshold voltage of the spike generator and the capacitance of the capacitor.

Abstract: A neural network device includes: a time-scheme spiking neuron model that outputs a signal when an internal state quantity that evolves over time in accordance with a signal input clock time, becomes a threshold value or more; and a delay unit that outputs a signal obtained by changing, by a set time, a spike clock time that is represented by the output signal of the time-scheme spiking neuron model as a relative clock time with respect to a reference clock time.

Abstract: A neural network device narrows down, from among respective time segments from when a spike is received to when a next spike is received, a candidate of a time segment including a firing timing of a spiking neuron, a membrane potential of the spiking neuron during a period from when a spike is received to when a next spike is received being represented by a monotonic function of time, a firing condition of the spiking neuron being represented by a comparison between the membrane potential and a threshold.

Abstract: A computation apparatus that includes a spiking neuron model. A spiking neuron model varies an index value of a signal output based on an input condition of a signal during an input time interval and outputs, based on the index value, a signal during an output time interval that starts after the input time interval ends.

Abstract: A computation apparatus includes a spiking neuron model that: varies, for each of a plurality of time intervals, an index value of a signal output based on an input condition of a signal in the time interval; detects an occurrence timing of a prescribed event relating to the index value; and outputs a signal at a timing that is within a first time interval and that is in accordance with the occurrence timing of the prescribed event within a second time interval. The first time interval is included in the plurality of time intervals. The second time interval is included in the plurality of time intervals and is a time interval further in past than the first time interval.

Abstract: A machine learning device includes: an input unit that acquires input data; an intermediate calculation unit that performs calculation on the input data a plurality of times; a weighting unit that performs weighting on an output of the intermediate calculation means for each of the plurality of times; an output unit that outputs output data based on a result of the weighting by the weighting means; and a learning unit that performs learning of a weight obtained by the weighting by the weighting means.

Abstract: Input unit to which a voltage is applied, current output unit that outputs a high level current or a low level current in response to the voltage applied to input unit, and stochastic circuit unit that, in response to the voltage applied to input unit, changes a probability that the high level current or the low level current is output from current output unit, in accordance with a sigmoid function used in a mathematical model of a neural activity are included.

Abstract: A spiking neural network system includes: a time-based spiking neural network; and a learning processing unit that causes learning of the spiking neural network to be performed by supervised learning using a cost function, the cost function using a regularization term relating to a firing time of a neuron in the spiking neural network.

Abstract: A neural network device includes: a neuron model unit configured as a non-leaky integrate-and-fire spiking neuron and a spiking neuron with which a postsynaptic current is represented using a step function, the neuron model unit being fired once at most in one process of a neural network to indicate an output of the neural model unit itself at firing timing; and a transfer processing unit that transfers information between the neuron model unit.

Abstract: Input unit to which a voltage is applied, current output unit that outputs a high level current or a low level current in response to the voltage applied to input unit, and stochastic circuit unit that, in response to the voltage applied to input unit, changes a probability that the high level current or the low level current is output from current output unit, in accordance with a sigmoid function used in a mathematical model of a neural activity are included.