[HTML][HTML] Why neurons have thousands of synapses, a theory of sequence memory in neocortex
J Hawkins, S Ahmad - Frontiers in neural circuits, 2016 - frontiersin.org
Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each
pyramidal neuron receives input from thousands of excitatory synapses that are segregated …
pyramidal neuron receives input from thousands of excitatory synapses that are segregated …
[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 …
[图书][B] Statistical physics of spin glasses and information processing: an introduction
H Nishimori - 2001 - books.google.com
Spin glasses are magnetic materials. Statistical mechanics, a subfield of physics, has been a
powerful tool to theoretically analyze various unique properties of spin glasses. This book is …
powerful tool to theoretically analyze various unique properties of spin glasses. This book is …
Chaotic neural networks
K Aihara, T Takabe, M Toyoda - Physics letters A, 1990 - Elsevier
A model of a single neuron with chaotic dynamics is proposed by considering the following
properties of biological neurons:(1) graded responses,(2) relative refractoriness and (3) …
properties of biological neurons:(1) graded responses,(2) relative refractoriness and (3) …
[图书][B] Neural networks: an introduction
B Müller, J Reinhardt, MT Strickland - 2012 - books.google.com
Neural Networks presents concepts of neural-network models and techniques of parallel
distributed processing in a three-step approach:-A brief overview of the neural structure of …
distributed processing in a three-step approach:-A brief overview of the neural structure of …
[PDF][PDF] Neural network fundamentals with graphs, algorithms, and applications
P Liang, NK Bose - Mac Graw-Hill, 1996 - ggnindia.dronacharya.info
NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
Page 1 NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND …
Page 1 NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND …
Time structure of the activity in neural network models
W Gerstner - Physical review E, 1995 - APS
Several neural network models in continuous time are reconsidered in the framework of a
general mean-field theory which is exact in the limit of a large and fully connected network …
general mean-field theory which is exact in the limit of a large and fully connected network …
Neural network modelling
JW Clark - Physics in Medicine & Biology, 1991 - iopscience.iop.org
The author reviews recent developments in neural networks that are of general interest to
physicists engaged in biological and biomedical research. Examples are examined of …
physicists engaged in biological and biomedical research. Examples are examined of …
Associative memory in a network of 'spiking'neurons
W Gerstner, JL van Hemmen - Network: Computation in Neural …, 1992 - Taylor & Francis
The Hopfield network provides a simple model of an associative memory in a neuronal
structure. It is, however, based on highly artificial assumptions, especially the use of formal …
structure. It is, however, based on highly artificial assumptions, especially the use of formal …
Robust computation with rhythmic spike patterns
Information coding by precise timing of spikes can be faster and more energy efficient than
traditional rate coding. However, spike-timing codes are often brittle, which has limited their …
traditional rate coding. However, spike-timing codes are often brittle, which has limited their …