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
Chaotic balanced state in a model of cortical circuits
Neural Computation
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
Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Computational models for neuroscience: human cortical information processing
Computational models for neuroscience: human cortical information processing
On Intelligence
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
Confabulation Theory: The Mechanism of Thought
Confabulation Theory: The Mechanism of Thought
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Selectivity and Stability via Dendritic Nonlinearity
Neural Computation
Neural Computation
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A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.