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thumb (詳細はneural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. In the context of spiking neural networks, the current activation level (modeled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and then either firing or decaying over time. Various ''coding methods'' exist for interpreting the outgoing ''spike train'' as a real-value number, either relying on the frequency of spikes, or the timing between spikes, to encode information. ==Beginnings== The first scientific model of a spiking neuron was proposed by Alan Lloyd Hodgkin and Andrew Huxley in 1952. This model describes how action potentials are initiated and propagated. Spikes, however, are not generally transmitted directly between neurons. Communication requires the exchange of chemical substances in the synaptic gap, called neurotransmitters. The complexity and variability of biological models have resulted in various neuron models, such as the integrate-and-fire (1907), FitzHugh–Nagumo model (1961–1962) and Hindmarsh–Rose model (1984). From the information theory point of view, the problem is to propose a model that explains how information is encoded and decoded by a series of trains of pulses, i.e. action potentials. Thus, one of the fundamental questions of neuroscience is to determine if neurons communicate by a rate or temporal code. Temporal coding suggests that a single spiking neuron ''can replace hundreds of hidden units on a sigmoidal neural net.'' 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Spiking neural network」の詳細全文を読む スポンサード リンク
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