Sentence examples for bottom-up interaction from inspiring English sources

This approach visualized the bottom-up interaction of individual agents to respond to other agents within the system.

Hence, the training algorithm must account for the top-down and the bottom-up interaction terms while learning the parameters of DBM.

To account for the top-down and bottom-up interaction terms, the structure of the HDLA model is altered while training according to the strategy already illustrated in Sect.

Similarly, the structure of the third layer RBM is modified in such a way that it involves two copies of hidden layer units h(^{(3)}) and the respective weight matrix W(^{(3)}) to compensate for the lack of bottom-up interactions from RBM-2.

The coupled system is adaptive and dynamically organizes itself to reflect complex bottom-up interactions between individual stakeholders, their contexts and the constraints driving their decisions even as they implement or respond to top-down decisions pertaining to the adoption of sustainable practices.

This result suggests an extremely rapid comparison of expected and incoming visual information and gives an upper temporal bound for theories of top-down and bottom-up interactions in object recognition.

Provided that match and mismatch of stimulus and prediction evoke distinct neural responses [5], an early difference between ERPs for predictable and unpredictable words represents an upper bound for the latency of top-down and bottom-up interactions.

If replicable across a wide range of tasks, our finding provides a critical temporal constraint for theories of top-down and bottom-up interactions as well as novel insights about the efficiency of stimulus encoding.

These can be encoded in bottom-up interactions between sensory and association areas, and top-down interactions for behavioral modulation.

These findings show that context can be encoded in large-scale bottom-up interactions from the posterior temporal cortex to the anterior temporal cortex and the lateral and medial PFC.

Our findings demonstrate the organization of a "context" network showing context is encoded in large-scale bottom-up interactions among distributed brain areas, and could later be reactivated top down to correlated eye movements.