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.

. We present a connectionist realization of parameterized schemas that can model high-level sensory-motor processes and be a candidate representation for implementing reactive behaviors. The connectionist realization involves a number of ideas including the use of focal-clusters and feedback loops to control a distributed process without a central controller and the expression and propagation of dynamic bindings via temporal synchrony. We employ a uniform mechanism for interaction between schemas, low-level somatosensory and proprioceptive processes, and high-level reasoning and memory processes. Our representation relates to work in connectionist models of rapid --- reflexive --- reasoning and also suggests solutions to several problems in language acquisition and understanding. 1 Introduction Performing goal-directed behavior in an uncertain and dynamic environment requires an agent to use representations that tightly couple action and reaction and that are highly responsive to envi...

... a connectionist semantic network that can perform inheritance and recognition in time proportional to the depth of the conceptual hierarchy; shruti --- a connectionist knowledge representation and inference system that can encode a large number of facts, rules, and a type hierarchy, and perform a class of first-order inferences with extreme efficiency, and shruti-cm5 an implementation of shruti on the CM-5 that can encode over half a million rules, facts, and types and respond to reflexive queries within a few hundred milliseconds.

Contract No. DASW01-97-C-0038 The views, opinions, and/or findings contained in this report are those of the authors and should not be construed as an official Department of the Army position, policy, or decision. REPORT DOCUMENTATION PAGE i

this article are of varying nature: position summaries, individual research summaries, historical accounts, discussion of controversial issues, etc. We have not attempted to connect the various pieces together, or to organize them within a coherent framework. Despite this, we think, the reader will find this collection useful.

this article are of varying nature: position summaries, individual research summaries, historical accounts, discussion of controversial issues, etc. No attempt was made to connect up the various pieces, nor to organize them in a coherent order. Despite this, we think the reader will find this collection useful.

Abstract not found