Sentence examples for temporal traces from inspiring English sources

For example, Jonker and Treur [18] proposed a formalism for mental states and their properties by describing their semantics in temporal traces, thus accounting for their dynamic changes during interactions.

Therefore, attractively, protein interaction modules and their relations with other proteins above can be thought as the dynamical markers (or temporal traces) of cell cycles in phase transitions.

Because baseline blood flow was close to the system noise level, temporal traces were normalized to the average of the second half of the first 2.5% CO2 period.

The temporal traces of the single glomeruli data [ Fig. 3 c,f)] allow to analyze possible temporal components of the olfactory code, like response delay or oscillatory responses as reported in other animals [ 22].

It is therefore obvious that we cannot expect that a single STDP rule, be it defined in the framework of temporal traces outlined above or in a more biophysical framework, would hold for all experimental preparations and across all neuron and synapse types.

All in all, along with the improvement of bicluster enumeration and sparse causal network inference, the proposed CPM can both detect unknown phase transitions in real biological systems, and identify the candidate functional cascade dynamics with temporal traces (or dynamical markers) during the transformation of a biological system.

For this reason, purely associative learning with no temporal trace was used in layer 1 of VisNet (Rolls and Milward 2000).

Hence, the varying relation with protein complex C1 can be a candidate temporal trace for functional specificity in the second cell cycle.

Various biological bases for this temporal trace have been advanced as follows: The precise mechanisms involved may alter the precise form of the trace rule which should be used.

A key property of VisNet is the way that it learns whatever can be learned at every stage of the network that is invariant as an image transforms in the natural world, using the temporal trace learning rule.

Indeed, these measures of similarity are likely to benefit from supervised training, as has been found (Khaligh-Razavi and Kriegeskorte 2014), whereas the similarity structure of models such as VisNet that utilizes a temporal trace rule will depend on the exact similarity structure of the input across time, which needs to be taken into account in such assessments.