Can spiking neural network takes the place of current neural network architecture?
What is the advantages and disadvantages of these two computation architecture?
Spiking neural networks is not a new idea. There are software packages with which you can use spiking neural networks, although they are not so widely used compared to the some more well-known ANNs (MLP, CNN, RNN,...). Your question cannot be simply answered because spiking neural network algorithms and other ANNs can exist in many variants and they can be enhanced too. In addition we should not talk about two computation architectures, since there are many slightly different neural net algorithms. One algorithm can be better solving one problem and an other algorithm solving an other problem. There are experimental studies showing results where SNN performed better than e.g. MLP-NN. Spiking neural networks mimic biologic neural networks more closely compared to other ANNs. The various models of spiking neurons offer different level of similarity to the biological nerve cell.
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks.[1] In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks.[1] In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer
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