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Spiking Neural Network
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Geliefert zwischen Mi., 28.01.2026 und Do., 29.01.2026
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High Quality Content by WIKIPEDIA articles! Spiking neural networks (SNNs) fall into the third generation of 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.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786131189067
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- EAN 9786131189067
- Format Fachbuch
- Titel Spiking Neural Network
- Herausgeber Betascript Publishing
- Anzahl Seiten 96
- Genre Mathematik
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