A connectionist approach to word sense disambiguation
A connectionist approach to word sense disambiguation
Unified theories of cognition
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
The design and implementation of massively parallel knowledge representation and reasoning systems: a connectionist approach
Structured connectionist models
The handbook of brain theory and neural networks
Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing
Hybrid Neural Systems, revised papers from a workshop
Rule-based reasoning in connectionist networks
Rule-based reasoning in connectionist networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Neural Networks and Structured Knowledge: Rule Extraction andApplications
Applied Intelligence
Segmenting state into entities and its implication for learning
Emergent neural computational architectures based on neuroscience
Biological grounding of recruitment learning and vicinal algorithms in long-term potentiation
Emergent neural computational architectures based on neuroscience
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Segmenting State into Entities and Its Implication for Learning
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
A connectionist computational model for epistemic and temporal reasoning
Neural Computation
Connectionist computations of intuitionistic reasoning
Theoretical Computer Science
Connectionist modal logic: Representing modalities in neural networks
Theoretical Computer Science
A (somewhat) new solution to the variable binding problem
Neural Computation
Real Time Machine Deduction and AGI
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A connectionist cognitive model for temporal synchronisation and learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A connectionist knowledge based system using coarse-coded distributed representations
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Connectionist predicate logic model with parallel execution of rule chains
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Connectionist mechanisms for cognitive control
Neurocomputing
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
A fault tolerant neuro-fuzzy inference system: using coarse-coded distributed representations
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
A connectionist model for predicate logic reasoning using coarse-coded distributed representations
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Fewer epistemological challenges for connectionism
CiE'05 Proceedings of the First international conference on Computability in Europe: new Computational Paradigms
Computational models of inductive reasoning using a statistical analysis of a Japanese corpus
Cognitive Systems Research
Recurrent networks for structured data - A unifying approach and its properties
Cognitive Systems Research
Artificial development of connections in SHRUTI networks using a multi objective genetic algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
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 achallenge 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 thischallenge by demonstrating how a neurally plausible network can encodea large body of semantic and episodic facts, systematic rules,and knowledge about entities and types, and yet perform a wide range ofexplanatory and predictive inferences within a few hundred milliseconds.Relational structures (frames, schemas) are represented in SHRUTIby clusters of cells, and inference in SHRUTI correspondsto a transient propagation of rhythmic activity over such cell-clusterswherein dynamic bindings are represented by the synchronous firingof appropriate cells. SHRUTI encodes mappings acrossrelational structures using high-efficacy links that enable the propagationof rhythmic activity, and it encodes items in long-term memory ascoincidence and coincidence-error detector circuits that become activein response to the occurrence (or non-occurrence) of appropriatecoincidences in the on going flux of rhythmic activity. Finally, “understanding” in SHRUTI corresponds toreverberant and coherent activity along closed loops of neural circuitry.Over the past several years, SHRUTI has undergone severalenhancements that have augmented its expressiveness and inferential power.This paper describes some of these extensions that enable SHRUTIto (i) deal with negation and inconsistent beliefs, (ii) encode evidentialrules and facts, (iii) perform inferences requiring the dynamic instantiationof entities, and (iv) seek coherent explanations of observations.