Add to ORKGOrganization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Alternate years
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
About the book: Connectionist Models of Learning, Development and Evolution comprises a selection of...
In this work we introduce the basic concepts of neural networks, their learning paradigms and learni...
Contents : Ch. 1. Introduction ; Ch. 2. Pattern Association Memory ; Ch. 3. Autoassociation Memory ;...
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.I...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
This collection of articles responds to the urgent need for timely and comprehensive reviews in a mu...
How are neural circuits organized and tuned to achieve stable function and produce robust behavor? T...
Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, F...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
In focus of this dissertation is the theoretical background of the neural networks with a descriptio...
Neural networks, here, mean mathematical models of biological neural networks composed of neurons. T...
Abstract. The majority of articial neural networks are static and life-less and do not change themse...
We present the main aspects of mathematical models for computational neuroscience, with emphasis on ...
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, deter...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
About the book: Connectionist Models of Learning, Development and Evolution comprises a selection of...
In this work we introduce the basic concepts of neural networks, their learning paradigms and learni...
Contents : Ch. 1. Introduction ; Ch. 2. Pattern Association Memory ; Ch. 3. Autoassociation Memory ;...
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.I...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
This collection of articles responds to the urgent need for timely and comprehensive reviews in a mu...
How are neural circuits organized and tuned to achieve stable function and produce robust behavor? T...
Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, F...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
In focus of this dissertation is the theoretical background of the neural networks with a descriptio...
Neural networks, here, mean mathematical models of biological neural networks composed of neurons. T...
Abstract. The majority of articial neural networks are static and life-less and do not change themse...
We present the main aspects of mathematical models for computational neuroscience, with emphasis on ...
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, deter...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
About the book: Connectionist Models of Learning, Development and Evolution comprises a selection of...
In this work we introduce the basic concepts of neural networks, their learning paradigms and learni...