A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method which is performable by using a neural network, the method comprising: determining a current activation of an artificial neuron based on a previous activation of the artificial neuron and an input signal received via an input synapse of the artificial neuron; determining a change amount of a first activation based on the determined current activation and a second activation that corresponds to an event that previously occurred due to the artificial neuron; determining whether a new event occurs based on a first threshold and the determined change amount of the first activation; and transmitting, to an output synapse, an output signal that corresponds to the new event in response to an occurrence of the new event. 2. The method of claim 1, wherein the new event occurs in response to a crossing of the first threshold by the determined change amount of the first activation. 3. The method of claim 1, wherein the output signal includes a sign bit that indicates a direction of a crossing of the determined change amount and the first threshold. 4. The method of claim 1, further comprising: receiving a second threshold that corresponds to a previous layer connected via the input synapse, wherein the determining of the current activation comprises determining the current activation based on the previous activation, the input signal, and the second threshold. 5. The method of claim 1, wherein the output signal includes the determined change amount of the first activation approximated as a predetermined degree of a bit precision. 6. The method of claim 5, wherein the predetermined degree of the bit precision is adjusted based on at least one from among a degree of requirement accuracy and a volume of available resources. 7. The method of claim 1, wherein the first threshold is adjusted based on a number of events that occur during a predetermined time interval. 8. The method of claim 7, wherein the first threshold increases in response to the number of the events that occur during the predetermined time interval exceeding a predetermined third threshold, the first threshold decreases in response to the number of the events that occur during the predetermined time interval being less than a predetermined fourth threshold, and the third threshold is greater than the fourth threshold. 9. The method of claim 1, further comprising: updating the first threshold based on the determined current activation after the output signal is transmitted. 10. The method of claim 9, wherein the first threshold is adjusted based on at least one from among a fixed step, a logarithmic step, and an order of magnitude. 11. The method of claim 1, further comprising: storing the determined current activation. 12. The method of claim 1, further comprising: receiving a control signal that indicates a predetermined operation mode; receiving a framed input signal that corresponds to the indicated operation mode via the input synapse; and determining the current activation based on the framed input signal. 13. The method of claim 1, wherein the neural network includes at least one from among an artificial neural network, a fully connected network, a deep convolutional network, a recurrent neural network, and a spiking neural network. 14. A method which is performable by using a neural network, the method comprising: determining a current activation of an artificial neuron based on a previous activation of the artificial neuron and an input signal received via an input synapse of the artificial neuron; determining whether an event occurs in response to a first cluster that includes the previous activation and a second cluster that includes the determined current activation; and transmitting, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event. 15. The method of claim 14, wherein the event occurs in response to the first cluster differing from the second cluster. 16. The method of claim 14, wherein the output signal includes at least one change bit that indicates at least one from among a change direction of at least one from among the first cluster and the second cluster and a change amount of the at least one from among the first cluster and the second cluster. 17. A method which is performable by using a recurrent neural network, the method comprising: obtaining a first input delta vector that corresponds to a first time based on a difference between a second input vector that corresponds to a second time that is earlier than the first time and a first input vector the corresponds to the first time; obtaining a hidden state delta vector at the corresponds to the second time based on a difference between a third hidden state vector that corresponds to a third time that is earlier than the second time and a second hidden state vector that corresponds to the second time; determining at least one parameter of the recurrent neural network based on a multiplication of a first weight that corresponds to the first input vector and a second weight that corresponds to the obtained first input delta vector, and a multiplication of a third weight that corresponds to the second hidden state vector and a fourth weight that correspond to the obtained hidden state delta vector; and determining a first hidden state vector that corresponds to the first time based on the determined at least one parameter of the recurrent neural network. 18. The method of claim 17, wherein the determined at least one parameter of the recurrent neural network includes at least one from among a value of a reset gate, a value of an update gate, and a value of an output hidden state vector. 19. The method of claim 17, wherein the obtaining of the first input delta vector comprises: determining the difference between the second input vector and the first input vector as the first input delta vector in response to the difference between the second input vector and the first input vector being greater than a predetermined threshold; and determining a zero vector as the first input delta vector in response to the difference between the second input vector and the first input vector being less than the predetermined threshold. 20. The method of claim 17, wherein the obtaining of the first input delta vector comprises: obtaining a second reference vector that corresponds to the second time based on a second input delta vector that corresponds to the second time; and obtaining the first input delta vector based on a difference between the obtained second reference vector and the first input vector. 21. The method of claim 20, wherein the obtaining of the second reference vector comprises: determining the second input delta vector as the second reference vector in response to the second input delta vector being greater than a predetermined threshold; and determining a third reference vector that corresponds to the third time as the second reference vector in response to the second input delta vector being less than the predetermined threshold. 22. The method of claim 17, wherein the obtaining of the hidden state delta vector comprises: determining the first input delta vector as the difference between the second input vector and the first input vector in response to the difference between the second input vector and the first input vector being greater than a predetermined threshold; and determining the first input delta vector as a zero vector in response to the difference between the second input vector and the first input vector being less than the predetermined threshold. 23. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1. 24. An electronic device comprising: a plurality of processing components that respectively correspond to artificial neurons, wherein each of the processing components includes a respective processor and a respective memory configured to store respective computer-readable instructions, and wherein for each respective processor, when the corresponding instructions are executed by the respective processor, the respective processor is configured to: determine a current activation based on a previous activation and an input signal received via an input link, determine a change amount of a first activation based on the current activation and a second activation that corresponds to an event that previously occurred, determine whether a new event occurs based on a first threshold and the determined change amount of the first activation, and transmit, to an output link, an output signal that corresponds to the new event in response to an occurrence of the new event. 25. The electronic device of claim 24, wherein the new event occurs in response to a crossing of the first threshold by the determined change amount of the first activation. 26. The electronic device of claim 24, wherein the output signal includes a sign bit that indicates a direction of a crossing of the determined change amount and the first threshold. 27. The electronic device of claim 24, wherein each respective processor is further configured to receive a second threshold that corresponds to a previous layer connected via the input link, and to determine the current activation based on the previous activation, the input signal, and the second threshold. 28. The electronic device of claim 24, wherein the output signal includes the determined change amount of the first activation approximated as a predetermined degree of a bit precision. 29. The electronic device of claim 28, wherein the predetermined degree of the bit precision is adjusted based on at least one from among a degree of requirement accuracy and a volume of available resources. 30. The electronic device of claim 24, wherein the first threshold is adjusted based on a number of events that occur during a predetermined time interval. 31. The electronic device of claim 30, wherein the first threshold increases in response to the number of the events that occur during the predetermined time interval exceeding a predetermined third threshold, the first threshold decreases in response to the number of the events that occur during the predetermined time interval being less than a predetermined fourth threshold, and the third threshold is greater than the fourth threshold. 32. The electronic device of claim 24, wherein each respective processor is configured to update the first threshold based on the corresponding determined current activation after the output signal is transmitted. 33. The electronic device of claim 32, wherein the first threshold is adjusted based on at least one from among a fixed step, a logarithmic step, and an order of magnitude. 34. The electronic device of claim 24, wherein each respective memory is configured to store the corresponding determined current activation.