Exact(2)
Retrospective causal inference works by iteratively pruning inputs from the history of the execution, and coping with divergent histories by reasoning about the functional equivalence of events.
We show that while HVC-RA connections undergo significant strengthening and pruning, inputs from LMAN remain largely unchanged throughout.
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PhenoLink allows decreasing huge combinations of possible experimental tests by pruning input data and prioritizing identified links.
They can be used during the system design phase to prune inputs that are irrelevant to the system output(s).
In addition, Sreenivas et al., in [17], Roche, in [18], and Wang et al., in [19], developed the methods for pruning the input-output at the same time.
In [24], the FFTDIF−DIT−TD modality (that we here refer to as DFTDIF−DIT−Pr) was proposed as an alternative technique for pruning the input and/or the output of DFTs.
The combination of strengthening and pruning HVC input to RA led to a dramatic reduction in LMAN's capacity to drive rendition-to-rendition variability in RA neurons.
Importantly, all our simulations showed that strengthening and pruning HVC input to RA decreases variability in RA firing irrespective of whether there are other concomitant changes in the circuit.
Some of them prune the input of a specific FFT algorithm, others prune the output, and just a few can prune the input and output (input-output) at the same time.
One method prunes the input variables by the cross-correlation method, while the other prunes the support vectors according to the derived relationship among SVM parameters.
They recommended turning to the method proposed by Sreenivas et al., in [17], i.e., to prune the input and output of a power of two length FFTs.
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