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4.3 Emergentist Cognitive Architectures 71

critical role of building and maintaining a model of the state of the world. In a vision processing
context, for example, it allows for powerful unsupervised classification. If shown a variety of
real-world scenes, it will automatically form internal structures corresponding to the various
natural categories of objects shown in the scenes, such as trees, chairs, people, etc.; and also
the various natural categories of events it sees, such as reaching, pointing, falling. And, as will
be discussed below, it can use feedback from DeSTIN’s action and critic networks to further
shape its internal world-representation based on reinforcement signals.

Benefits of DeSTIN for Perception Processing

DeSTIN’s perceptual network offers multiple key attributes that render it more powerful than
other deep machine learning approaches to sensory data processing:

1. The belief space that is formed across the layers of the perceptual network inherently
captures both spatial and temporal regularities in the data. Given that many applications
require that temporal information be discovered for robust inference, this is a key advantage
over existing schemes.
2. Spatiotemporal regularities in the observations are captured in a coherent manner (rather
than being represented via two separate mechanisms)
3. All processing is both top-down and bottom-up, and both hierarchical and heterarchical,
based on nonlinear feedback connections directing activity and modulating learning in mul-
tiple directions through DeSTIN’s cortical circuits
4. Support for multi-modal fusing is intrinsic within the framework, yielding a powerful state
inference system for real-world, partially-observable settings.
5. Each node is identical, which makes it easy to map the design to massively parallel platforms,
such as graphics processing units.

Points 2-4 in the above list describe how DeSTIN’s perceptual network displays its own
“cognitive synergy” in a way that fits naturally into the overall synergetic dynamics of the overall
CogPrime architecture. Using this cognitive synergy, DeSTIN’s perceptual network addresses
a key aspect of general intelligence: the ability to robustly infer the state of the world, with
which the system interacts, in an accurate and timely manner.

4.3.1.3 DeSTIN for Action and Control

DeSTIN’s perceptual network performs unsupervised world-modeling, which is a critical aspect
of intelligence but of course is not the whole story. DeSTIN’s action network, coupled with the
perceptual network, orchestrates actuator commands into complex movements, but also carries
out other functions that are more cognitive in nature.
For instance, people learn to distinguish between cups and bowls in part via hearing other
people describe some objects as cups and others as bowls. To emulate this kind of learning,
DeSTIN’s critic network provides positive or negative reinforcement signals based on whether
the action network has correctly identified a given object as a cup or a bowl, and this signal
then impacts the nodes in the action network. The critic network takes a simple external “degree
of success or failure” signal and turns it into multiple reinforcement signals to be fed into the
multiple layers of the action network. The result is that the action network self-organizes so

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