[PDF] The Perception–Action Hierarchy and its Implementation Using Binons (Binary Neurons). Postproceedings of the 10th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2019 (Tenth Annual Meeting of the BICA Society), Procedia Computer Science, Elsevier.
Abstract: The perception–action hierarchy contains a model of the environment as experienced based on what has been recognized and done. Binons (binary neurons) can be used to represent and implement this hierarchy. Binons are simple deterministic artificial neural nodes that represent relationships. They have two source nodes and are reused by zero or more target nodes. Binons are general purpose components that interact in an object-oriented fashion. The two types of binons are spatial and temporal. Spatial binons represent simultaneously occurring patterns of percepts and actions. Temporal binons represent sequential patterns of percepts and actions. Two kinds of temporal binons are used to learn and control behaviour. They are the action and expectation control binons. They are equivalent to command neurons in neuroscience, production rules in cognitive architectures, or the forward model in motor control when combined together. Learning takes place in the three stages of babbling, practicing and automaticity. The resulting hierarchy is a transparent, compositional, unsupervised, continuously growing, deep learning artificial neural network. The hierarchy is part of the Adaptron cognitive architecture.
[PDF] Presentation - Powerpoint slides  with comments, Version 4, March 2020.
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