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.