Sentence examples for higher-level constraints from inspiring English sources

High-level association cortices not only receive feedforward signals from sensory regions but also anticipate and reciprocate these inputs with backward connections conferring context-specificity and higher-level constraints (i.e. predictions) (Rao and Ballard, 1999; Friston, 2003, 2005; Angelucci and Bressloff, 2006).

The input of this method is a sequence of higher level constraints, for example pitch.

These individual controllers, however, do not have the knowledge of higher-level coupling constraints and therefore require coordination.

This co-occurrence effect could therefore be used to predict the presence of a syllable- or word-final grammatical morpheme, consistent with higher-level linguistic constraints.

The overall picture is by far more complex than previously thought; biological interactions that take place at lower molecular levels are characterized by unpredictable nonlinear dynamics and become tightly influenced by higher-level organizational constraints [ 12].

Both constraints and higher-level processes may be responsible for generating trends, which may be passive as well as driven and may be influenced by a number of factors.

Deep architectures can find higher-level representations, thus can potentially capture relevant higher-level abstractions.

Species generally showed higher ED scores than higher-level taxa.

The constraint analysis enforcing the higher-level waterbird topology recovered by Livezey and Zusi's [4] morphological anlaysis resulted in the recovery of three MPTs, each of which were 1243 steps (21 steps longer than the unconstrained MPTs), with consistency and retention indices of 0.434 and 0.847, respectively.

The constraint analysis enforcing the higher-level waterbird topology recovered by Hackett et al.'s [29] molecular analysis recovered three MPTs, each of which were 1371 steps (149 steps longer than the unconstrained MPTs), with consistency and retention indices of 0.393 and 0.820, respectively.

Instead, each subsystem solves its own local optimization problem and only reports limited information to a higher-level controller to ensure the satisfaction of the global constraints which are often defined as limitations on the utilization or production on some shared resources or raw materials.