If you know the most likely sequence for this series of inputs, you will use this knowledge to decide how to classify the ambiguous input.

Large-scale relationships are stored at the top of the hierarchy and small-scale relationships are stored toward the bottom.

Second, unlike most other neural networks, an autoassociative memory can be designed to store sequences of patterns, or temporal patterns.

But what if an unexpected pattern arrives, an unexpected note? Or what if we see something that does not belong on a face? The unexpected pattern is automatically passed to the next higher cortical region.

An image enters your pupil, gets inverted by your lens, hits your retina, and creates a spatial pattern. This pattern gets relayed to your brain. People tend to think that there’s a little upside-down picture of the world going into your visual areas, but that’s not how it works. There is no picture. It’s not an image anymore. Fundamentally, it is

“I experience many different patterns. Sometimes I can’t predict what pattern I will see next. But these patterns are definitely related to one another. They always occur together, and I can reliably jump between them. So whenever I see any of these events, I will refer to them by a common name. It is this group name, not the individual patterns, t

Sometimes we know exactly what is going to happen, other times our expectations are distributed among several possibilities.

Notice the memory of sequences allows you not only to resolve ambiguity in the current input, but also to predict which input should happen next.

The overall idea is that cells want any information that will help them predict when they will be driven from below.