Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Fast sigmoidal networks via spiking neurons
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Spikes: exploring the neural code
Spikes: exploring the neural code
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Learning Temporally Encoded Patterns in Networks of SpikingNeurons
Neural Processing Letters
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
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The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.