Titlebook: Weakly Connected Neural Networks; Frank C. Hoppensteadt,Eugene M. Izhikevich Book 1997 Springer Science+Business Media New York 1997 biolo

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Frank C. Hoppensteadt,Eugene M. Izhikevichnikation aufgrund der Digitalisierung bei Entscheidern und Belegschaft im Unternehmen sowie von anderen Anspruchs- und Konsumentengruppen gestiegen. Die digitale Beschleunigung des Informationstransports, „rund-um-die-Uhr“-Verfügbarkeiten von Kommunikations- bzw. IT-Angeboten, der immer kürzer werde

动脉

dry-eye

Local Analysis of Singularly Perturbed WCNNsfurcation parameter and external input from sensor organs, respectively. As before, we assume that all functions F., G., which represent the dynamics of each neuron, and all .. and .. which represent connections between the neurons, are as smooth as necessary for our computations.

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Multiple Cusp Bifurcationpe NN) for certain choices of the parameters b.,…, ... We use Cohen and Grossberg’s convergence theorem to show that the canonical model can also operate as a multiple attractor neural network (MA-type NN).

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场所

Bifurcations in Neuron Dynamicsn neuron dynamics is the transition from rest to periodic spiking activity. Generation of a single spike is a bifurcation phenomenon too: It can result when the rest potential is close to a bifurcation point, which is often referred to as its threshold value. We discuss these and similar issues in t

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Neural Networksity experiences a bifurcation: The equilibrium corresponding to the rest potential loses stability or disappears, and the neuron fires. This bifurcation is local, but it results in a nonlocal event — the generation of an action potential, or spike. These are observable global phenomena. To model the

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Local Analysis of Singularly Perturbed WCNNslow variables, respectively; τ is a slow time; and ′ = .. The parameters ε and μ are small, representing the strength of synaptic connections and ratio of time scales, respectively. The parameters λ ∈ Λ and ρ ∈ R. have the same meaning as in the previous chapter: They represent a multidimensional bi