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The connectivity function J represents the intracortical connections between neurons and (I_{mathrm{thalamus}}) the thalamic input.
In their article Potthast and Graben also work with a different functional space by assuming more regularity for the connectivity function W and then obtain more regularity for their solutions.
Once again the choice of a piece-wise constant connectivity function considerably simplifies further calculations.
We assume that the connectivity function satisfies the conditions (C1 - C2).
The connectivity function checks if a given set of blocks forms a path.
These instabilities depend on the expression of the delay function (and the connectivity function as well).
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The optimal functional regression equation (80% of variance) confirmed the importance of connectivity functions at [Gln6] and [Tyr8], as well as the shape of residues [Lys1] and [Pro4].
This chapter provides an overview on the connectivity functions of routers and switches on a network.
This sparsity is modelled with radial connectivity functions or kernels with non-central peaks.
This is an important factor to consider when using connectivity functions as an upscaling tool in catchment scale modeling.
In the following two parts, we study the solutions for different connectivity functions: an exponential function, Section 6.2, and a difference of Gaussians, Section 6.3.
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