The Spike Response Model is a generalization of the leaky integrate-and-fire model and
gives a simple description of action potential generation in neurons. Just as in the integrate …

Major achievements in designing and analyzing recurrent spiking neural networks intended
for modeling functional brain networks are reviewed. Key terms and definitions employed in …

Short-term synaptic dynamics differ markedly across connections and strongly regulate how
action potentials communicate information. To model the range of synaptic dynamics …

In this work, we consider recurrent neural networks of firing rate neurons supervisely trained
to generate multidimensional sequences of given configurations. We study dynamical …

Adaptive sensory processing influences the central nervous system's interpretation of
incoming sensory information. One of the functions of this adaptive sensory processing is to …

The representation of the natural-density, heterogeneous connectivity of neuronal network
models at relevant spatial scales remains a challenge for Computational Neuroscience and …

We review a reverse-engineering approach to cerebellar function that pays particular
attention to temporal aspects of neuronal interactions. This approach offers new vistas on …

In this paper a phenomenological model of spike-timing dependent synaptic plasticity
(STDP) is developed that is based on a Volterra series-like expansion. Synaptic weight …

In this chapter we define for various neural coding schemes formal models of computation in
networks of spiking neurons. The main results about the computational power of these …

In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an
elemental spiking neuron model that is capable of not only quantitatively reproducing spike …