In this section we examine the dynamics of the model in response to variations of the slow parameter ε.
We describe shaping the mnemonic landscape, how it acts as a probability density function to guide slow parameter dynamics, and how the parameters shape the network output.
For the case of relatively slow parameter variations the equations of motion are reduced by Krylov Bogoliubov averaging to those describing static instability of the response amplitude.
Many causes might exist for the neural network to start oscillating, e.g., a slow parameter or an external factor might cause a bifurcation [2], or a perturbation might force the system to a different attractor [3].
The parameters used in Tree-Puzzle were set to the JTT substitution model, the mixed model of rate heterogeneity with one invariant and eight gamma rate categories, and the exact and slow parameter estimation.
The parameters were set to the JTT substitution model [ 69], mixed model of rate heterogeneity with one invariable and eight Gamma rate categories, and the exact and slow parameter estimation.
The evolutionary distances were computed by TREE-PUZZLE where the parameters were set to the JTT substitution model, the mixed model of rate heterogeneity with one invariant and eight gamma rate categories, and using the exact and slow parameter estimation.
To achieve higher sensitivity, regions that do not align with a given program can be identified and aligned using more sensitive (and usually much slower) parameters or alternative programs.
Therefore, slow changing parameters, or parameters that have lesser effect on the circulation in the computer model, are held constant so that changes in more physiologically relevant and sensitive parameters can be tracked.
where I i,j) is the intensity of the pixel, (i,j), the denominator is the maximum intensity taken over all pixels (x,y) from the selected slices, and S is a slowing parameter (S = 3).
In our study bursting behavior was engineered by describing slow changing parameters.
