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- W2243928500 abstract "Overview: A Brain-Emulating Cognition and Control Architecture (BECCA) is presented. It is consistent with the hypothesized functions of pervasive intra-cortical and cortico-subcortical neural circuits. It is able to reproduce many salient aspects of human voluntary movement and motor learning. It also provides plausible mechanisms for many phenomena described in cognitive psychology, including perception and mental modeling. Both “inputs” (afferent channels) and “outputs” (efferent channels) are treated as neural signals; they are all binary (either on or off) and there is no meaning, information, or tag associated with any of them. Although BECCA initially has no internal models, it learns complex interrelations between outputs and inputs through which it bootstraps a model of the system it is controlling and the outside world. BECCA uses two key algorithms to accomplish this: S-Learning and Context-Based Similarity (CBS). S-Learning: S-Learning is an experience-based learning approach in which sequences of states (the set of inputs and outputs at discrete time intervals) are recorded directly into a library. During goal-directed behavior, the library is searched for sequences that begin at the current state and end at a goal state. These successful sequences are replicated as nearly as possible in order to achieve the goal again. SLearning’s lack of an a priori model of the system allows it to learn hard non-linear, time-delayed, and stochastically varying dynamics. S-Learning is demonstrated controlling a simulated 7 degree of freedom robotics arm. Context-Based Similarity: CBS uses the library created by the S-Learning algorithm to form abstract concepts. CBS assumes that states are likely to be similar if they are preceded and followed by the same state prefix and state postfix. For instance, given two sequence of three states, A-B-C and A-Z-C, the states B and Z are assumed to be related. Taken over a large library, CBS can form an extensive set of abstract concepts based only on raw observations. These concepts can be then be used as the inputs to another instance of S-Learning and CBS which in turn create higher-level con-" @default.
- W2243928500 created "2016-06-24" @default.
- W2243928500 creator A5068167226 @default.
- W2243928500 date "2008-07-01" @default.
- W2243928500 modified "2023-09-26" @default.
- W2243928500 title "A Unified Architecture for Cognition and Motor Control Based on Neuroanatomy Psychophysical Experiments and Cognitive Behaviors." @default.
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