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For both general and particular networks, such as paths, cycles, meshes, tori and trees, we derive tight bounds on the virtual diameter (the maximum hop count for a connection) as a function of the network capacity (the maximum load of a physical link).

Fig. 6 For each type of neural connection from edge cells, an (H_{mathrm{f},c}) function is computed that describes the strength of that connection as a function of the relative phase between the edge cell and the oscillator where forcing is applied.

Fig. 5 Relative strengths (alpha_{rc}/alpha_{r}) of different connection types as a function of the connection length r.

Similarly, Fig. 9B shows connection strengths, again as a function of the connection length, for both ascending and descending connections where (A_{a}=0.006), (A_{d}=0.0004), (lambda_{a}=0.75), and (lambda_{d}=4).

This hypothesis requires models of effective connectivity, in which connection strengths vary as a function of the associative strength predicted by the learning model.

The human cortex grows in size, develops complexity, makes synaptic connections and modifies as a function of the quality and quantity of sensory experience.

In panel B, the number of connections, misconnections, and their difference as a function of the cut-off tracking radius R, for each protein under study are shown.

Entrainment ranges as a function of the connection strength and forcing position are presented in Sect. 4.

Furthermore, the frequency distribution of nodes as a function of the number of connections decreased monotonically (Fig. 2).

The mutual information, I, between a neuron i and the network increases as a function of the neuron's total connection strength, s′ = ∑ j| J ij|.

Fig. 9 Entrainment ranges as a function of the forcing position for varying intersegmental connections.