Top Down Bottom up Brain Models

Bottom-up, top-down

Bottom-Up Beats Top-Down

In PEPA, the calculation of the transitions enabled by a process accounts for a large part of the time for the state space exploration of the underlying Markov chain. Unlike other approaches based on recursion, we present a new technique that is iterative—it traverses the process’ binary tree from the sequential components at the leaves up to the root. Empirical results shows that this algorith...

Computational models: Bottom-up and top-down aspects

Computational models of visual attention have become popular over the past decade, we believe primarily for two reasons: First, models make testable predictions that can be explored by experimentalists as well as theoreticians; second, models have practical and technological applications of interest to the applied science and engineering communities. In this chapter, we take a critical look at ...

When bottom-up meets top-down.

Studying the intriguing question of how words or pictures are encoded, processed and stored has been a long-standing problem in cognition. Both the written word frog, and a picture of the animal, can lead to an observer to say /frog/, or otherwise to the association that frogs leap, croak, need water, etc. Two different types of account have been formulated to explain word and picture processin...

Combining Bottom-Up and Top-Down

We motivate the formulation of market equilibrium as a mixed complementarity problem which explicitly represents weak inequalities and complementarity between decision variables and equilibrium conditions. The complementarity format permits an energy-economy model to combine technological detail of a bottom-up energy system with a second-best characterization of the overall economy. Our primary...