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We consider a randomly connected neural network with linear threshoM elements which update in discrete time steps. The two main features of the network are: (1) equally distributed and purely excitatory connections and (2) synaptic depression after repetitive firing. We focus on the time evolution of the expected network activity. The four types of(More)
Part 5 reviewed diierent models of neurons and provided a survey of appropriate simulation tools. Part 6 will terminate this series`From neuron to network: Measurement, analysis and modeling' with a tutorial section describing an implementation of a neuron model with Matlab/Simulink and a typical application of it.
The rst four parts of this series gave an introduction to methods of measuring and analysis of signals in biological neural networks. The remaining two parts will consider aspects of methods in neuronal modeling. Part 5 will review diierent levels of modeling and will give a survey of appropriate simulation tools. Finally, part 6 will show in a tutorial(More)
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