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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.
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We derive the blocking probabilities of connections as a function of bandwidth, traffic demand and the available number of virtual paths based on the Erlang loss formula for all service classes.
All 40 models shared the same endogenous network structure but differed in the modulation of these endogenous connections as a function of condition.
Fig. 5 Relative strengths (alpha_{rc}/alpha_{r}) of different connection types as a function of the connection length r.
Neurons within a lamina show distinct dendritic architecture, chemoarchitecture and patterns of connection as well as function.
In accordance with the considerations above, we investigated whether the learning-related changes in visual cortex responses could be explained by a simple model of effective connectivity, in which the strength of A1 → V1 connection changed as a function of the associative strength predicted by the RW model.
Similarly, bidirectional connection probability as a function of distance was calculated by dividing the existing number of bidirectional connections by the number of all possible connections given a distance.
Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency.
Additionally, we examined local efficiency and connection probability as a function of distance (A13).
However, local efficiency and connection probability as a function of distance between neurons showed discrepancy from what the model predicted (A13).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com