Sentence examples for input encoding from inspiring English sources

The two-unit model is an approximation of the megamap driven by an external input encoding two locations, denoted by (textbf{x}_{1}) and (textbf{x}_{2}).

While a megamap in either mode is similarly robust to a noisy or incomplete external input, there are clear differences between the modes when the network is driven by conflicting external input encoding multiple locations in the environment.

In this situation, small megamaps operating in the WTA mode effectively suppress the input encoding one location and fully represent the second location, but large megamaps operating in the combinatorial mode robustly represent both locations through two co-stable activity bumps (Fig. 1(c) and (d)).

We evaluated several types of input encoding based on protein features, evolutionary and functional information.

We then constructed SVM classifiers by combining some of the best single features for input encoding.

The results suggest that classifier performance can be enhanced by combining certain sequence features for input encoding.

All the above operations continue until all the input encoded samples are decoded.

It exploits the statistical distribution of input encoded codewords and adopts a nonuniform partition decoding scheme for the detector.

Our results show that copies of Bcd target genes respond to the activator input regardless of whether such an input encodes spatial information (i.e., as a Bnuc profile along the A-P axis) or not (i.e., as Bnuc fluctuations at given A-P positions).

When developing models for single domains, the input encodes only the information for the peptide sequence.

In brief, it is assumed that levels closer to the sensory input encode faster dynamics of the stimulus than levels further away from the sensory input.