AIMC Topic: Association Learning

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Cross-validation of matching correlation analysis by resampling matching weights.

Neural networks : the official journal of the International Neural Network Society Dec 22, 2015
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weig...
Probability Support Vector Machine Association Learning Humans Models, Theoretical Neural Networks, Computer
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Memory Stacking in Hierarchical Networks.

Neural computation Dec 14, 2015
Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to stud...
Memory Association Learning Synapses Models, Neurological Neurons Action Potentials Brain Computer Simulation Neural Networks, Computer Humans
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Associative memory realized by a reconfigurable memristive Hopfield neural network.

Nature communications Jun 25, 2015
Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield...
Central Nervous System Association Learning Memory Animals Neural Networks, Computer Pattern Recognition, Automated Algorithms Computer Simulation
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A rational model of function learning.

Psychonomic bulletin & review Mar 3, 2015
Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We pr...
Normal Distribution Association Learning Linear Models Problem Solving Transfer, Psychology Bayes Theorem Machine Learning Humans Concept Formation Models, Psychological
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Improving Recall in Sparse Associative Memories That Use Neurogenesis.

Neural computation Feb 14, 2025
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their nois...
Models, Neurological Neurons Animals Mental Recall Association Learning Humans Neurogenesis Algorithms Memory Neural Networks, Computer
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A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

IEEE transactions on neural networks and learning systems Nov 1, 2017
In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the...
Algorithms Computer Simulation Neural Networks, Computer Humans Memory Time Factors Association Learning
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