Unsupervised learnable neuron model with nonlinear interaction on dendrites
Y Todo, H Tamura, K Yamashita, Z Tang - Neural Networks, 2014 - Elsevier
Recent researches have provided strong circumstantial support to dendrites playing a key
and possibly essential role in computations. In this paper, we propose an unsupervised …
and possibly essential role in computations. In this paper, we propose an unsupervised …
Neurons with multiplicative interactions of nonlinear synapses
Neurons are the fundamental units of the brain and nervous system. Developing a good
modeling of human neurons is very important not only to neurobiology but also to computer …
modeling of human neurons is very important not only to neurobiology but also to computer …
Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction
An artificial neural network (ANN) that mimics the information processing mechanisms and
procedures of neurons in human brains has achieved a great success in many fields, eg …
procedures of neurons in human brains has achieved a great success in many fields, eg …
A dendritic neuron model with adaptive synapses trained by differential evolution algorithm
A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution
(DE) algorithm training is proposed. According to the signal transmission order, a DNM can …
(DE) algorithm training is proposed. According to the signal transmission order, a DNM can …
Efficient dendritic learning as an alternative to synaptic plasticity hypothesis
S Hodassman, R Vardi, Y Tugendhaft, A Goldental… - Scientific Reports, 2022 - nature.com
Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local
adaptation between two connecting neurons and forms the foundation of machine learning …
adaptation between two connecting neurons and forms the foundation of machine learning …
Improved neuronal models for studying neural networks
RB Stein, KV Leung, D Mangeron, MN Oğuztöreli - Kybernetik, 1974 - Springer
Previous neuronal models used for the study of neural networks are considered. Equations
are developed for a model which includes: 1) a normalized range of firing rates with …
are developed for a model which includes: 1) a normalized range of firing rates with …
[图书][B] Neural Computing-an introduction
R Beale, T Jackson - 1990 - books.google.com
Neural computing is one of the most interesting and rapidly growing areas of research,
attracting researchers from a wide variety of scientific disciplines. Starting from the basics …
attracting researchers from a wide variety of scientific disciplines. Starting from the basics …
Introduction to artificial neural network
XS Zhang, XS Zhang - Neural Networks in Optimization, 2000 - Springer
Artificial neural networks or simply “neural nets” go by many names such as connectionist
models, parallel distributed processing models, and neuromorphic systems. Whatever …
models, parallel distributed processing models, and neuromorphic systems. Whatever …
[图书][B] Introduction to the theory of neural computation
JA Hertz - 2018 - taylorfrancis.com
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …
A survey on dendritic neuron model: Mechanisms, algorithms and practical applications
Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …
the development of dendritic computation. Creating an ideal neuron model is crucial for …