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Author(s): Carpenter, G.A. | Grossberg, S. |
Year: 1988
Citation: In J.L. Davis, R.W. Newburgh, & E.J. Wegman (Eds.), Brain Structure, Learning, and Memory, AAAS Selected Symposia Series, 105, 233-290.
Abstract: A theory is developed of how recognition categories can be learned in response to a temporal stream of input patterns. Interactions between an attentional subsystem and an orienting subsystem enable the network to self-stabilize its learning, without an external teacher, as the code becomes globally self-consistent. Category learning is thus determined by global contextual information in this system. The attentional subsystem learns bottom-up codes and top-down templates, or expectancies. The internal representations formed in this way stabilize themselves against recoding by matching the learned top-down templates against input patterns. this matching process detects structural pattern properties in addition to local feature matches. The top-down templates can also suppress noise in the input patterns, and can subliminally prime the network to anticipate a set of input patterns. Mismatches activate an orienting subsystem, which resets incorrect codes and drive a rapid search for new or more appropriate codes. As the learned code becomes globally self-consistent, the orienting subsystem is automatically disengaged and the memory consolidates. After the recognition categories for a set of input patterns self-stabilize, those patterns directly access their categories without any search or recoding on future recognition trials. A novel pattern exemplar can directly access an established category if it shares invariant properties with the set of familiar exemplars of that category. Several attentional and nonspecific arousal mechanisms modulate the course of search and learning. Three types of attentional mechanism - priming, gain control, and vigilance - are distinguished. Three types of nonspecific arousal are also mechanistically characterized. The nonspecific vigilance process determines how fine the learned categories will be. If vigilance increases due, for example, to a negative reinforcement, then the system automatically searches for and learns finer recognition categories. The learned to-down expectancies become more abstract as the recognition categories become broader. The learned code is a property of network interactions and the entire history of input pattern presentations. The interactions generate emergent rules such as a Weber Law Rules, a 2/3 Rule, and an Associative Decay Rule. No serial programs or algorithmic rule structures are use. The interactions explain and predict properties of evoked potentials (processing negativity, mismatch negativity, P300). Malfunction of the orienting system causes a formal amnesic syndrome analogous to that caused by malfunction of medial temporal brain structures: limited retrograde amnesia, long-range anterograde amnesia, failure of memory consolidation, effective priming, and defective reactions to novel cues. Comparisons with alternative theories of amnesia are made.
Topics:
Biological Learning,
Machine Learning,
Models:
ART 1,
