[工程科技]Advances in SHRUTI - A neurally motivated model of relational knowledge representation and.pdf

1 Appeared in Applied Intelligence , 11: 79-108, 1999 Advances in Shruti — A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony Lokendra Shastri International Computer Science Institute 1947 Center Street Berkeley, CA 94704 shastri@ Abstract We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remark- able human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using