Fast sigmoidal networks via spiking neurons
Neural Computation
On the complexity of learning for spiking neurons with temporal coding
Information and Computation
On computing Boolean functions by a spiking neuron
Annals of Mathematics and Artificial Intelligence
The lack of a priori distinctions between learning algorithms
Neural Computation
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Resonant Spike Propagation in Coupled Neurons with Subthreshold Activity
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A spiking neural network model of an actor-critic learning agent
Neural Computation
Segmentation and Edge Detection Based on Spiking Neural Network Model
Neural Processing Letters
Rebound spiking as a neural mechanism for surface filling-in
Journal of Cognitive Neuroscience
Improved Izhikevich neurons for spiking neural networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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Spiking neural networks have been called the third generation of neural networks. Their main difference with respect to the previous two generations is the use of realistic neuron models. Their computational power has been well studied with respect to threshold gates and sigmoidal neurons. However, biologically realistic models of spiking neurons can produce behaviors that can be computationally relevant, but their power has not been assessed in the same way. This paper studies the computational power of neurons with different behaviors based on the previous analyses conducted by Maass and Schmitt. The studied behaviors are rebound spiking, resonance and bursting. The results of the analysis are presented. A theoretical motivation for this study is presented and a discussion is done on the possible implications of the findings for using networks of spiking neurons for performing computations.