SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference

[1]  Lokendra Shastri,et al.  Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony , 1999, Applied Intelligence.

[2]  Jerome A. Feldman,et al.  Dynamic connections in neural networks , 1990, Biological Cybernetics.

[3]  William McCune,et al.  OTTER 3.3 Reference Manual , 2003, ArXiv.

[4]  Lokendra Shastri,et al.  A computationally efficient abstraction of long-term potentiation , 2002, Neurocomputing.

[5]  L. Shastri.  Episodic memory and cortico–hippocampal interactions , 2002, Trends in Cognitive Sciences.

[6]  Lokendra Shastri,et al.  A Connectionist Encoding of Parameterized Schemas and Reactive Plans , 2002 .

[7]  Sheila A. McIlraith,et al.  Theorem Proving with Structured Theories , 2001, IJCAI.

[8]  Eyal Amir,et al.  Theorem Proving with Structured Theories (Preliminary Report)* , 2001, Electron. Notes Discret. Math..

[9]  John Arnold Kalman.  Automated Reasoning With Otter , 2001 .

[10]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[11]  Richard Waldinger,et al.  A Guide to Snark , 2000 .

[12]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[13]  Lokendra Shastri,et al.  Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing , 1998, Hybrid Neural Systems.

[14]  Jacques Sougné,et al.  Connectionism and the problem of multiple instantiation , 1998, Trends in Cognitive Sciences.

[15]  John E. Hummel,et al.  Distributed representations of structure: A theory of analogical access and mapping. , 1997 .

[16]  D. R. Mani,et al.  Massively parallel knowledge representation and reasoning: Taking a cue from the brain , 1997, Parallel Processing for Artificial Intelligence 3.

[17]  D. R. Mani,et al.  The design and implementation of massively parallel knowledge representation and reasoning systems: a connectionist approach , 1996 .

[18]  Lokendra Shastri,et al.  Temporal synchrony, dynamic bindings, and Shruti: A representational but nonclassical model of reflexive reasoning , 1996, Behavioral and Brain Sciences.

[19]  Keith Stenning,et al.  Extension of the temporal synchrony approach to dynamic variable bindingin a connectionist inference system , 1995, Knowl. Based Syst..

[20]  Tony A. Plate,et al.  Holographic reduced representations , 1995, IEEE Trans. Neural Networks.

[21]  James Henderson,et al.  Connectionist syntactic parsing using temporal variable binding , 1994 .

[22]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[23]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[24]  Lokendra Shastri,et al.  A Computational Model of Tractable Reasoning - Taking Inspiration from Cognition , 1993, IJCAI.

[25]  T. Bliss,et al.  A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.

[26]  D. R. Mani,et al.  Reflexive Reasoning with Multiple Instantiation in a Connectionist Reasoning System with a Type Hierarchy , 1993 .

[27]  R. Ratcliff,et al.  Inference during reading. , 1992, Psychological review.

[28]  Ron Sun,et al.  On Variable Binding in Connectionist Networks , 1992 .

[29]  John A. Barnden,et al.  Encoding techniques for complex information structures in connectionist systems , 1991 .

[30]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[31]  Michael G. Dyer,et al.  High-level Inferencing in a Connectionist Network , 1989 .

[32]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[33]  W. Kintsch.  The role of knowledge in discourse comprehension: a construction-integration model. , 1988, Psychological review.

[34]  J. Keenan,et al.  The effects of causal cohesion on comprehension and memory , 1984 .

[35]  Eugene Charniak,et al.  Passing Markers: A Theory of Contextual Influence in Language Comprehension* , 1983 .

[36]  D. Norman.  Learning and Memory , 1982 .

[37]  Scott E. Fahlman,et al.  NETL: A System for Representing and Using Real-World Knowledge , 1979, CL.

[38]  M. Just,et al.  Cognitive processes in comprehension , 1977 .

[39]  Marvin Minsky,et al.  Semantic Information Processing , 1968 .

[40]  R. Bellman.  Dynamic Programming , 1957, Science.