08-17-2017, 12:10 AM
To enable the development and execution of complex behaviors in autonomous robots
involving adaptation and learning, sophisticated software architectures are required.
The neural schema architecture provides such a system, supporting the development
and execution of complex behaviors, or schemas [3][2], in a hierarchical and layered
fashion [9] integrating with neural network processing.
In general, schema theory helps define brain functionality in terms of concurrent
activity of interacting behavioral units called schemas. Schema-based modeling may
be specified purely on behavioral data (ethology), while becoming part of a neural
based approach to adaptive behavior when constrained by data provided by, e.g., the
effects of brain lesions upon animal behavior (neuroethology). Schema modeling
provides a framework for modeling at the purely behavioral level, at the neural
network level or even below [28]. In terms of neural networks, neural schema theory
provides a functional/structural decomposition, in strong contrast with models which
employ learning rules to train a single, otherwise undifferentiated, neural network to
respond as specified by some training set. Neural schema-based modeling proceeds
at two levels: (1) model behavior in terms of schemas, interacting functional units;
(2) implementation of schemas as neural networks based on neuroanatomical and
neurophysiological studies. What makes the linking of structure and function so
challenging is that, in general, a functional analysis proceeding "top-down" from
some overall behavior need not map directly into a "bottom up" analysis proceeding
upwards from the neural circuitry
