Pulsed Neural Networks

TL;DR: The brain's default state: self-organized oscillations in rest and sleep, and perturbation of the default patterns by experience.

TL;DR: Spike timing-dependent modifications, together with selective spread of synaptic changes, provide a set of cellular mechanisms that are likely to be important for the development and functioning of neural networks.

TL;DR: Three different uses of a recurrent neural network as a reservoir that is not trained but instead read out by a simple external classification layer are compared and a new measure for the reservoir dynamics based on Lyapunov exponents is introduced.

TL;DR: It is demonstrated that temporal coding requires significantly less neurons than instantaneous rate-coding, and a supervised learning rule, \emph{SpikeProp}, akin to traditional error-backpropagation, is derived.

TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.

TL;DR: The principles of neural science as mentioned in this paper have been used in neural networks for the purpose of neural network engineering and neural networks have been applied in the field of neural networks, such as:

TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.

TL;DR: It is shown that networks of spiking neurons are, with regard to the number of neurons that are needed, computationally more powerful than these other neural network models based on McCulloch Pitts neurons, respectively, sigmoidal gates.