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Dynamic connections in neural networks
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CONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at…
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A Learning Algorithm for Boltzmann Machines**The research reported here was supported by grants from the System Development Foundation. We thank Peter Brown, Francis Crick, Mark Derthick, Scott Fahlman, Jerry Feldman, Stuart Geman, Gail Gong, John Hopfield, Jay McClelland, Barak Pearlmutter, Harry…
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Tunnel vision will not suffice
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Making a middling mousetrap
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Self-organizing neural models of categorization, inference and synchrony
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Dynamic-binding theory is not plausible without chaotic oscillation
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A step toward modeling reflexive reasoning
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Neuromotor Synergies as a Basis for Coordinated Intentional Action
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Binding and multiple instantiation in a distributed network of spiking nodes
inferred← citeshttps://doi.org/10.1080/09540090902954188openalex-snapshot
Variable binding by synaptic strength change
inferred← citeshttps://doi.org/10.1080/09540099108946588openalex-snapshot
Encoding Techniques for Complex Information Structures in Connectionist Systems
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Principles of Node Growth and Node Pruning
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Reflexive Reasoning with Multiple Instantiation in a Connectionist Reasoning System with a Type Hierarchy
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Optimizing Turning Parameters Based on Correlation Pruning Neural Networks
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Prediction of cutting parameters based on improved neural networks
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Application of Sensitivity Pruning Neural Networks in Surface Roughness Prediction
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Application of Genetic Algorithm Optimizing Neural Networks in Machining a Group of Holes
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Performance Analysis of an Improved Single Neuron Adaptive PID Control
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.287112openalex-snapshot
Dynamic neural networks with the use of divide and conquer
inferred← citeshttps://doi.org/10.1109/ijcnn.2003.1223348openalex-snapshot
Using temporal binding for connectionist recruitment learning over delayed lines
inferred← citeshttps://doi.org/10.1109/ijcnn.2009.5178686openalex-snapshot
Sequential hierarchical recruitment learning in a network of spiking neurons
inferred← citeshttps://doi.org/10.1109/iscsct.2008.191openalex-snapshot
Study on Integrated Intelligent Control with Hierarchical Structure
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Application of Recursive Predict Error Neural Networks in Mechanical Propertise Forecasting
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Application of Structure Self-organizing Neural Networks for Quality Testing of Gear Wheel
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THE IMAGERY DEBATE VIEWED FROM A CONNECTIONIST AND LANGUAGE GROUNDING PERSPECTIVE
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A temporal connectionist approach to natural language
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Computing with structured connectionist networks
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Evaluating Information Transfer Between Auditory Cortical Neurons
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Sistemas computacionais para atenção visual Top-Down e Bottom-up usando redes neurais artificiais
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Memorization and Association on a Realistic Neural Model
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Derivation of Encoding Characteristics of Layer II Cerebral Cortex
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Rules and Variables in Neural Nets
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A (Somewhat) New Solution to the Variable Binding Problem
inferred← citeshttps://doi.org/10.1162/neco.2009.08-08-851openalex-snapshot
Experience-Induced Neural Circuits That Achieve High Capacity
inferred← citeshttps://doi.org/10.1162/neco_a_00195openalex-snapshot
Self-Organization of Topographic Bilinear Networks for Invariant Recognition
inferred← citeshttps://doi.org/10.1162/neco_a_00357openalex-snapshot
The Hippocampus as a Stable Memory Allocator for Cortex
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Computing with Connections In Visual Recognition of Origami Objects*
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A Learning Algorithm for Boltzmann Machines*
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Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks
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Massive parallelism in artificial intelligence
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A Probabilistic Strategy for Understanding Action Selection
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CONSENSUS: A Statistical Learning Procedure in a Connectionist Network.
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Selective attention and the acquisition of spatial semantics
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Faculty Opinions recommendation of A probabilistic strategy for understanding action selection.
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Neuro-Expert Systems with Structured Multi-Layered Networks
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Working Memory as Programmable Fast Weight Computation
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Learning and Representation in Connectionist Models
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
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and goes through ORCID: icsacinstitute.org/submit.