Hybrid Neural Systems

An Overview of Hybrid Neural Systems.- An Overview of Hybrid Neural Systems.- Structured Connectionism and Rule Representation.- Layered Hybrid Connectionist Models for Cognitive Science.- Types and Quantifiers in SHRUTI - A Connectionist Model of Rapid Reasoning and Relational Processing.- A Recursive Neural Network for Reflexive Reasoning.- A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning.- Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference.- Towards a Hybrid Model of First-Order Theory Refinement.- Distributed Neural Architectures and Language Processing.- Dynamical Recurrent Networks for Sequential Data Processing.- Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.- Combining Maps and Distributed Representations for Shift-Reduce Parsing.- Towards Hybrid Neural Learning Internet Agents.- A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations.- Large Patterns Make Great Symbols: An Example of Learning from Example.- Context Vectors: A Step Toward a "Grand Unified Representation".- Integration of Graphical Rules with Adaptive Learning of Structured Information.- Transformation and Explanation.- Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks.- Symbolic Rule Extraction from the DIMLP Neural Network.- Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.- Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification.- High Order Eigentensors as Symbolic Rules in Competitive Learning.- Holistic Symbol Processing and the Sequential RAAM: An Evaluation.- Robotics, Vision and Cognitive Approaches.- Life, Mind, and Robots.- Supplementing Neural Reinforcement Learning with Symbolic Methods.- Self-Organizing Maps in