Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.
As multimedia training environments become increasingly popular, many have placed multimedia systems diametrically opposite to intelligent tutoring systems (ITSs) in a schema of educational applications of computers. Some argue, however, that multimedia systems lack the goaloriented teaching approach of an ITS. Multimedia could, however, be integrated into the architecture of an ITS, such that information is easy to create and to retrieve as networks of interconnected multimedia documents, together with the different means of delivering information to the student. This paper illustrates, through the discussion pertaining to the implementation of ARISTOTLE, a tutoring system for zoology, how multimedia can be incorporated into an ITS.
Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.
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