Titlebook: Brain Dynamics; An Introduction to M Hermann Haken Book 2008Latest edition Springer-Verlag Berlin Heidelberg 2008 Computational Neuroscienc

Projection

The Neuron – Building Block of the Brainlly reach the synapses that, in a way to be discussed below, transfer the information to another neuron. In order to be able to model the functioning of a neuron, we have to deal with these processes in more detail. In this chapter we will be satisfied by a qualitative discussion with only a few mat

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The Lighthouse Model. Many Coupled Neurons, in particular, on the phase-locked state, its stability and its stability limits. The example of two delay times will be presented in detail, but general results are also included. Spatial aspects, namely phase-waves, are dealt with in Sect. 6.9. The remaining part of this chapter deals with two s

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Many Neurons, General Case, Connection with Integrate and Fire Model the effect of delays and noise. We will treat the first-order and second-order differential equations for the dendritic currents using the same formalism so that we can compare the commonalities of and differences between the results of the two approaches. The network connections of excitatory or i

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Pattern Recognition Versus Synchronization: Pattern Recognition of the state vector is described by a set of differential equations which describe the time evolution of neuronal activity due to the internal dynamics (which are assumed to be very simple) and their interactions. The dynamics or, in other words, the temporal change of the state vector, can finish

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Conclusion and Outlooktate was established by means of a specific initial condition that does not seem unrealistic. We then studied the kind of stability, whether marginal or asymptotic. We recognized that this feature depends on the kind of perturbations, where we studied those that lead only to marginal stability as we

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978-3-642-09450-7Springer-Verlag Berlin Heidelberg 2008

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Brain Dynamics978-3-540-75238-7Series ISSN 0172-7389 Series E-ISSN 2198-333X

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