A recognition apparatus and a training method are provided. The recognition apparatus includes a memory configured to store a neural network including a previous layer of neurons, and a current layer of neurons that are activated based on first synaptic signals and second synaptic signals, the first synaptic signals being input from the previous layer, and the second synaptic signals being input from the current layer. The recognition apparatus further includes a processor configured to generate a recognition result based on the neural network. An activation neuron among the neurons of the current layer generates a first synaptic signal to excite or inhibit neurons of a next layer, and generates a second synaptic signal to inhibit neurons other than the activation neuron in the current layer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A recognition apparatus comprising: a memory configured to store a neural network comprising a previous layer of neurons, and a current layer of neurons that are activated based on first synaptic signals and second synaptic signals, the first synaptic signals being input from the previous layer, and the second synaptic signals being input from the current layer; and a processor configured to generate a recognition result based on the neural network, wherein an activation neuron among the neurons of the current layer generates a first synaptic signal to excite or inhibit neurons of a next layer, and generates a second synaptic signal to inhibit neurons other than the activation neuron in the current layer. 2. The recognition apparatus of claim 1, wherein the neurons of the current layer comprise spiking neurons. 3. The recognition apparatus of claim 1, wherein the activation neuron adjusts a membrane potential of the other neurons based on the first synaptic signals and the second synaptic signals. 4. The recognition apparatus of claim 1, wherein the activation neuron transmits the second synaptic signal to the other neurons based on a lateral inhibition coefficient of the activation neuron. 5. The recognition apparatus of claim 4, wherein a neuron receiving the second synaptic signal among the other neurons decreases a membrane potential of the neuron based on a membrane potential threshold of the neuron and the lateral inhibition coefficient. 6. The recognition apparatus of claim 1, wherein the previous layer comprises a group of neurons, and the neurons included in the group share a receptive field. 7. The recognition apparatus of claim 6, wherein the current layer comprises another group of neurons, the other group corresponding to the receptive field, and each of the neurons sharing the receptive field is connected to all of the neurons included in the other group corresponding to the receptive field. 8. The recognition apparatus of claim 6, wherein the processor is further configured to: determine an exponential decay term for the neurons included in the group; and determine a membrane potential of each of the neurons included in the group, based on the exponential decay term. 9. The recognition apparatus of claim 1, wherein a membrane potential of the neurons of the current layer is within a range based on a membrane potential threshold of the neurons of the current layer. 10. The recognition apparatus of claim 1, wherein the processor is further configured to change a membrane potential time constant value of neurons included in the neural network based on a result of a comparison between a magnitude of input signals that are input to the neuron network and a reference value. 11. The recognition apparatus of claim 10, wherein the processor is further configured to increase the membrane potential time constant value of the neurons included in the neural network in response to the magnitude of the input signals being less than a first reference value. 12. The recognition apparatus of claim 10, wherein the processor is further configured to decrease a membrane potential time constant value of neurons included in a first layer among previous layers of neurons in response to the magnitude of the input signals being greater than a second reference value. 13. The recognition apparatus of claim 1, wherein the first synaptic signals are input over time, and wherein a membrane potential of the neurons of the current layer changes based on a weight of synapses corresponding to the first synaptic signals. 14. The recognition apparatus of claim 1, wherein in response to the first synaptic signals not being input, a membrane potential of the neurons of the current layer is decreased at a rate over time. 15. The recognition apparatus of claim 1, wherein the activation neuron outputs a spike signal in response to a membrane potential of the activation neuron exceeding a membrane potential threshold of the activation neuron, and the membrane potential of the activation neuron is decreased in response to the spike signal being output. 16. The recognition apparatus of claim 15, wherein the membrane potential of the activation neuron is decreased by a value proportional to the membrane potential threshold of the activation neuron, or the membrane potential of the activation neuron is decreased by a rate proportional to the membrane potential threshold of the activation neuron. 17. The recognition apparatus of claim 1, wherein a value of a lateral inhibition coefficient of the activation neuron is determined based on a weight of synapses corresponding to each of the neurons of the current layer. 18. A training method comprising: applying training data to an input layer of a neural network, the training data comprising events over time; acquiring an output value of an output layer of the neural network; and training the neural network based on the output value and a desired value corresponding to the training data. 19. The training method of claim 18, wherein the neural network comprises a previous layer of neurons, and a current layer of neurons that are activated based on input synaptic signals that are input from the previous layer and a lateral inhibition signal that are input from a current layer, and an activation neuron among the neurons of the current layer excites or inhibits neurons of a next layer, and inhibits neurons other than the activation neuron in the current layer. 20. The training method of claim 18, wherein the training comprises: determining an output activity of a neuron included in the neural network based on input activities of the neuron, a membrane potential reset factor of the neuron, and a membrane potential threshold of the neuron; and determining a parameter of neurons included in the neural network based on the output activity of the neuron included in the neural network. 21. The training method of claim 20, wherein the parameter comprises at least one among the membrane potential threshold of the neuron, input synaptic weights of the neuron, output synaptic weights of the neuron, and a lateral inhibition coefficient of the neuron. 22. The training method of claim 21, wherein the training further comprises adjusting the output activity of the neuron based on the membrane potential reset factor of the neuron, the lateral inhibition coefficient of the neuron, a lateral discount factor of the neuron, and output activities of neurons other than the neuron in a group of neurons. 23. The training method of claim 22, wherein the lateral inhibition coefficient of the neuron is determined based on the input synaptic weights of the neuron. 24. The training method of claim 18, wherein the training comprises changing a membrane potential time constant value of neurons included in the neural network based on a result of a comparison between a magnitude of input signals that are input to the neural network and a reference value. 25. The training method of claim 24, wherein the changing comprises increasing the membrane potential time constant value of the neurons in response to the magnitude of the input signals being less than a first reference value. 26. The training method of claim 24, wherein the changing comprises decreasing a membrane potential time constant value of neurons included in a first layer among previous layers of neurons in response to the magnitude of the input signals being greater than a second reference value. 27. The training method of claim 18, wherein the training comprises: determining a loss that is propagated back to the neural network, based on the desired value and the output value; and determining a parameter of neurons included in the neural network based on the loss and a gradient descent scheme that is based on an output activity of the neurons. 28. The training method of claim 18, wherein the training comprises: comparing a sum of squares of input synaptic weights of a neuron included in the neural network to a weight reference value; determining a weight regulation rate based on a result of the comparing; and decreasing the input synaptic weights based on the weight regulation rate. 29. The training method of claim 28, wherein the determining of the weight regulation rate comprises: increasing the weight regulation rate in response to the sum of the squares being greater than the weight reference value; and decreasing the weight regulation rate in response to the sum of the squares being less than or equal to the weight reference value. 30. The training method of claim 18, wherein the training comprises regulating a membrane potential threshold of neurons included in the neural network based on whether the neurons are activated. 31. The training method of claim 30, wherein the regulating comprises increasing a membrane potential threshold of an activation neuron among the neurons at a rate. 32. The training method of claim 31, wherein the regulating comprises decreasing a membrane potential threshold of the neurons based on a number of neurons sharing a receptive field in a previous layer of neurons and a number of activation neurons included in the previous layer. 33. The training method of claim 18, wherein the regulating comprises increasing a synaptic weight corresponding to each of neurons included in the neural network in response to a membrane potential threshold of the neurons being less than a membrane potential reference value. 34. The training method of claim 18, wherein the training comprises initializing at least one among a membrane potential threshold and synaptic weights of neurons included in a layer of the neural network based on a number of synapses corresponding to each of the neurons. 35. The training method of claim 18, wherein the training comprises normalizing a backpropagation error so that an update value of synaptic weights of neurons included in the neural network is equal to an update value of a membrane potential threshold of the neurons. 36. A non-transitory computer-readable storage medium storing a program comprising instructions to cause a processor to perform the training method of claim 18. 37. A recognition apparatus comprising: a memory configured to store a neural network comprising a previous layer of first neurons, and a current layer of second neurons receiving first input signals and second input signals, the first input signals being received from the previous layer, and the second input signals being received from the current layer; and a processor configured to generate a recognition result based on the neural network, wherein an activation neuron among the second neurons: receives a first input signal among the first input signals; and generates a first output signal to excite or inhibit third neurons of a next layer, and generates a second output signal to inhibit neurons other than the activation neuron of the current layer, in response to a potential of the first input signal being greater than a threshold. 38. The recognition apparatus of claim 37, wherein each of the other neurons: receives the second output signal from the activation neuron; and decreases a potential of a respective one of the other neurons by a product of a potential threshold of the respective one of the other neurons and a lateral inhibition coefficient of the activation neuron.