Sentence examples for more efficient neural from inspiring English sources
No experiments have been done to see if eyeless fish in caves develop a more efficient neural network than eyed fish living in the dark.
But it was perfectly possible that this was the result of their skills, and not the cause: after all, practising any skill, be it music, sport, or a language, can lead the brain to build more efficient neural networks.
This is compatible with the cognitive reserve theory, which states that some life experiences result in more efficient neural functioning providing the individual a cognitive reserve that delays the onset of dementia manifestations.
In this section, we discuss how the adaptive gain control promotes a more efficient neural code for odors by considering the discrimination of pairs of odors.
This method results in a more efficient neural commitment and differentiation, which likely results from a better mimicry of the events that occur in the embryo.
Dendritic pruning and synapse elimination produce more efficient neural circuits that continue to be remodelled throughout life [ 32].
Consequently, NG2 cells are more efficient than neural stem cells from the subventricular zone in terms of differentiating into oligodendrocyte precursor cells, migrating to targeted axons, and participating in myelination.
While this is more work, it's also a more efficient use of neural real estate this way, complete records don't need to be stored.
The human brain tends to be a more regular and efficient neural architecture in the late stage of information processing.
The 2-digit addition problems used in the present study, which require maintaining and updating of verbal numbers in working memory, suggest that the negative correlation observed between the BOLD responses reflect more efficient use of neural and general cognitive resources in subjects with higher performance levels in L2 addition.
Now, Sze and her colleagues have approached the same problem from the opposite direction, with a battery of techniques for designing more energy-efficient neural networks.
