This expectation arises from the fact that the PhyloCSF score is actually a likelihood ratio, calculated as the log ratio of probabilities of observing a given set of triplet substitutions under a coding model of evolution versus a non-coding model of evolution.
Simultaneous absence of evidence of both models would a ratio of probabilities close to 1.0 and a log-ratio of 0. Instead, we find a median PhyloCSF score for the novel extensions to be -159.8 decibans, indicating that, on average, a non-coding model fits these extensions far better (10-fold) than a coding model.
We numerically maximize this probability over ρ C to obtain the likelihood of the coding model p C. We then evaluate the likelihood of the non-coding model p N in the same way, using Q N and an independent scale factor ρ N, and report the log-likelihood ratio as the result.
An adaptive coding model of neural function in prefrontal cortex.
These findings appear to be at odds with the asynchronous discrete coding model.
Here, we tested the predictions of the efficient coding model in both the psychophysical performance of humans and in the corresponding multivariate patterns of BOLD fMRI response.
It was also easier to apply the face-to-face coding model for use in analyzing the virtual setting than the other way around.
Instead of hand-crafted features, a new hierarchical spatio-temporal trajectory coding model is presented to learn and represent the local spatio-temporal trajectories.
In this work, we consider quantized state measurements relayed to a controller via a communication network adopting the standard network coding model.
Because the neuron databases are continuously expanding, it is inefficient to re-train the binary coding model from scratch when adding new neurons.
According to the coding model, we establish an associated case-based knowledgebase to carry out data search and inference application so as to shorten the working time.
