Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neurocomputing
Multiplying with synapses and neurons
Single neuron computation
NMDA-based pattern discrimination in a modeled cortical neuron
Neural Computation
Associative neural memories
Matching performance of binary correlation matrix memories
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Unsupervised learning
Spikes: exploring the neural code
Spikes: exploring the neural code
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Pulsed Neural Networks
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Parallel Models of Associative Memory
Parallel Models of Associative Memory
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Computational models for neuroscience: human cortical information processing
Computational models for neuroscience: human cortical information processing
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On Intelligence
A fast learning algorithm for deep belief nets
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Engines of the brain: the computational instruction set of human cognition
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Backpropagation applied to handwritten zip code recognition
Neural Computation
Neural Computation
Cluster Analysis
Why Does Unsupervised Pre-training Help Deep Learning?
The Journal of Machine Learning Research
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A learning machine, called a clustering interpreting probabilistic associative memory (CIPAM), is proposed. CIPAM consists of a clusterer and an interpreter. The clusterer is a recurrent hierarchical neural network of unsupervised processing units (UPUs). The interpreter is a number of supervised processing units (SPUs) that branch out from the clusterer. Each processing unit (PU), UPU or SPU, comprises ''dendritic encoders'' for encoding inputs to the PU, ''synapses'' for storing resultant codes, a ''nonspiking neuron'' for generating inhibitory graded signals to modulate neighboring spiking neurons, ''spiking neurons'' for computing the subjective probability distribution (SPD) or the membership function, in the sense of fuzzy logic, of the label of said inputs to the PU and generating spike trains with the SPD or membership function as the firing rates, and a masking matrix for maximizing generalization. While UPUs employ unsupervised covariance learning mechanisms, SPUs employ supervised ones. They both also have unsupervised accumulation learning mechanisms. The clusterer of CIPAM clusters temporal and spatial data. The interpreter interprets the resultant clusters, effecting detection and recognition of temporal and hierarchical causes.