Sentence examples for predictions of connectivity from inspiring English sources
We show that the approach recovers known neuron types in the retina and enables predictions of connectivity, better than simpler algorithms.
A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject.
We have found that combining the available data types allows us to discover cell types and microcircuitry that were known to exist in the systems based on decades of previous research and allows good prediction of connectivity.
By comparing with functional annotation information and gene expression data in Saccharomyces cerevisiae, we have validated that this biophysically motivated use of evolutionary conservation gives rise to dramatic improvement in prediction of regulatory connectivity and factor factor interactions compared to the use of a single genome.
Future work will test the model's predictions for connectivity in larger cortices to gain insight into how the regulation of axonal outgrowth may have evolved to achieve efficient and economical connectivity in larger brains.
We also investigate how the connectivity predictions of patch-based graphs have been assessed and emphasize the importance of empirical evaluation.
To further test our predictions of changes in functional connectivity due to sad mood induction and to confirm our exploratory ICA findings, we performed seed-based CCAs between major regions of interest (ROIs) in the 'paralimbic' and 'default mode' networks (see Materials & Methods).
Finally, we explore the benefits of A-fods-based A-fods-based A-fods-basedo datractographyting agreement of tractography predictions with connectivity estimates made using dinferent in-vivo modataties.
When applied to connectomics data for 950 neurons in the mouse retina, the algorithm generated predictions regarding cell types and patterns of connectivity.
This important feature of biological networks is significant for a credible prediction of essentiality when the factors of connectivity and betweenness centrality are utilized.
There is no difference between the connectivity distributions of complexed and non-complexed proteins in our data to justify the use of connectivity for complex prediction.
