%0 Journal Article %1 aceee2013spiking %D 2013 %E ACEEE, %J ACEEE Int. J. on Information Technology %K Driven Event Group Latency Network Neural Neuron Neuronal Plasticity SNN Selection Spiking Threshold classifier %N 1 %T Spiking Neural Networks As Continuous-Time Dynamical Systems: Fundamentals, Elementary Structures And Simple Applications %U /brokenurl#doi.searchdl.org/01/IJIT/3/1/1129.pdf %V 3 %X In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier.

@article{aceee2013spiking, abstract = {In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier. }, added-at = {2013-03-09T07:59:16.000+0100}, biburl = {https://www.bibsonomy.org/bibtex/220a55ff40338dfac75d5a8b6cb7e4473/ideseditor}, editor = {ACEEE}, interhash = {b784de31094f6b25f3fa93751d526cb9}, intrahash = {20a55ff40338dfac75d5a8b6cb7e4473}, journal = {ACEEE Int. J. on Information Technology}, keywords = {Driven Event Group Latency Network Neural Neuron Neuronal Plasticity SNN Selection Spiking Threshold classifier}, month = mar, number = 1, timestamp = {2013-03-09T07:59:16.000+0100}, title = {Spiking Neural Networks As Continuous-Time Dynamical Systems: Fundamentals, Elementary Structures And Simple Applications }, url = {/brokenurl#doi.searchdl.org/01/IJIT/3/1/1129.pdf}, volume = 3, year = 2013 }