Importantly, these relatively fast timescales match the temporal learning windows of neurons (<50 ms) [56], and hence, may render spike-timing dependent synaptic plasticity operant for learning informative activation patterns.
With the change the nature of the lectures, the adapted-teaching version became the first step in diverting the responsibility for learning informative content to the students.
To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies.
Together, these results indicate that monkeys selectively learn informative fragments incidentally as a part of category learning.
We have previously reported, using human psychophysical studies, that when subjects learn new object categories using whole objects, they incidentally learn informative fragments, even when not required to do so.
Our results indicate that the category learning in monkeys is similar to humans, at least in that monkeys also learn informative, intermediate-complexity fragments as a matter of course when they learn new object categories, even though the animals were not explicitly required to learn the fragments.
We hypothesized, on the basis of the aforementioned computational considerations and our previous results in humans [12], [13], that if the animals unknowingly learned informative object fragments during the training of learning the whole objects, then the animals must be able to perform the categorization task using the individual Main fragments, but not the Control fragments.
We finally show that our tissue/cell-type predictions are interpretable based on the biological processes enriched among learned informative genes.
Rather than predefining some specific set of annotations as informative about variant consequences, an alternative approach is to attempt to learn informative annotations, or combinations of annotations, by comparing known functional variants with variants for which there is no direct evidence of functional consequences.
By learning how informative each dataset is with respect to each gold standard, we reweighted the datasets, combined them to infer a single genome-wide functional network in each context of interest, and analyzed the resulting networks as detailed below to generate novel biological hypotheses.
To fully exploit this inspiration but avoid detecting AUs, we propose to automatically learn: (1) informative local appearance variation; (2) optimal way to combining local variation and (3) high level representation for final expression recognition.
