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Extra info for Artificial neural networks: Architectures and applications
Org/publications/dsn. pdf. 3. e. from the portrait phase of figure 7(b) to 7(c). From the point of view of dynamical systems, the transition corresponds to a bifurcation of the dynamical neuron, or a qualitative representation of the phase of the system. In general, neurons are excitable because they are close to bifurcations from rest to spiking activity. The type of bifurcation depends on the electrophysiology of the neuron and determines its excitable properties. Interestingly, although there are millions of different electrophysiological mechanisms of excitability and spiking, there are only four different types of bifurcation of equilibrium that a system can provide.
The K-sets mediate between the microscopic activity of small neural networks and the macroscopic activity of the brain. The topology includes excitatory and inhibitory populations of neurons and the dynamics is represented by ordinary differential equations (ODE) . 5772/54177 Biologically Plausible Artificial Neural Networks Figure 10. Representation of (b) KI and (c) KII sets by networks of (a) KO sets. Available at . The advantages of KIII pattern classifiers on artificial neural networks are the small number of training examples needed, convergence to an attractor in a single step and geometric increase (rather than linear) in the number of classes with the number of nodes.
The modification of the genetic code, that is, mutation, as well as the modification of the degree of affinity of receptors, however, is related to the target gene. The reason is that the modulation function of controller is better explained at some distance of the emission of neurotransmitter, therefore at the target. 2. A network simulation In table 3, a data set for a five-layer network simulation is presented . For the specifications displayed in table 3, the network architecture and its activated connections are shown in figure 18.