Abstract. A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in re...
Christian Boucheny, Richard R. Carrillo, Eduardo R...
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses su...
Robert A. Legenstein, Dejan Pecevski, Wolfgang Maa...
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
This paper introduces a new model of a spiking neuron with active dendrites and dynamic synapses (ADDS). The neuron employs the dynamics of the synapses and the active properties ...
This paper presents a framework for provisioning application and channel dependent quality of service in wireless networks. The framework is based on three di erent adaptation mec...
Javier Gomez, Andrew T. Campbell, Hiroyuki Morikaw...