PubMed · 4482943
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E W Fair. 1972. Improving your concentration capacity.. https://pubmed.ncbi.nlm.nih.gov/4482943/
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We propose a set of indices that characterize-on the basis of connectivity data-how a network node participates in a larger network and what roles it may take given the specific sub-network of interest. These Network Participation Indices are derived from simple graph theoretic measures and have the interesting property of linking local features of individual network components to distributed properties that arise within the network as a whole. We use connectivity data on large-scale cortical networks to demonstrate the virtues of this approach and highlight some interesting features that had not been brought up in previously published material. Some implications of our approach for defining network characteristics relevant to functional segregation and functional integration, for example, from functional imaging studies are discussed.
We study the stability of two types of mixed states in an oscillator neural network model. Patterns to be stored are represented by complex variables whose amplitudes are either 0 (silent state) or 1 (firing state) and whose phases represent the timing of firing. The mixed states are defined in terms of a certain type of average over some specified number s of memory patterns. We define two types of such mixed states, each of which corresponds to a different type of restriction placed on this set of s memory patterns. The stability of each type is investigated both theoretically and numerically. We find that only one type of a mixed state, which consists of temporally correlated patterns, is stable. Finally, we discuss a possible functional role of such mixed states in information processing.
This study investigated the unit of activation in on-line inferences. To explore whether the activation of inferences is a word-unit or a proposition-unit, a meaningfulness-decision task was devised as a priming measure. The task required participants to decide whether a series of short sentences was meaningful or not. The result of Experiment 1 suggested that only proposition-units were activated. A priming effect was observed in targets which described inferences both at word-level and proposition-level, but not in targets which described inferences at word-level only. In Experiment 2, a meaningfulness-decision task was administered to investigate word-based priming and to make sure that the result of Experiment 1 was not caused by characteristics of the task. In Experiment 3, a priming effect was found in targets which described inferences using alternative perspectives and words. These results suggest that the unit of activation in on-line inferences is proposition-units and that these activations relate to the proposition-semantic level, not the word or single-concept level.