Every indexed document

which makes it easy to map the design to massively parallel platforms

it can use feedback from DeSTIN’s action and critic networks to further

it will automatically form internal structures corresponding to the various

we can further simplify the expression such that

based on the past history of observations

and each of them comprises a certain ""spatiotemporal form"" recognized

each corresponding to a set of previously-observed

a statistical learning algorithm is used to predict subsequent states based on prior

which corresponds to a spatiotemporal region (nodes higher in the hierarchy corresponding

and attempt to construct belief states that capture regularities

and oftentimes pre-training. DeSTIN overcomes the limitations

this paradigm has its limitations; for instance

with the difference lying mainly in the DeSTIN control

general-purpose hierarchical control architecture. DeSTIN’s control hierarchy

which is often imperfect and always applicationdomain

not explicitly incorporating the hierarchical structure of the world in

most machine learning and pattern recognition systems are

building on prior work in the area of deep learning.

in which computation processes and memory storage are

and are somehow able to capture critical aspects of it in a way that allows for