| Thread Review (Newest First) |
| Posted by shishir.kadam - 08-17-2017, 12:10 AM |
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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 |