Advances in SHRUTI - A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony (1999)
| Venue: | Applied Intelligence |
| Citations: | 50 - 15 self |
BibTeX
@ARTICLE{Shastri99advancesin,
author = {Lokendra Shastri},
title = {Advances in SHRUTI - A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony},
journal = {Applied Intelligence},
year = {1999},
volume = {11},
pages = {79--108}
}
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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 remarkable 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 high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.
