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Information Rate Maximization over a Resistive Grid

Koeppl, H. (2006):
Information Rate Maximization over a Resistive Grid.
Vancouver, BC, IEEE, In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Abstract

The work presents the first results of the authors research on adaptive cellular neural networks (CNN) based on a global information theoretic cost-function. It considers the simplest case of optimizing a resistive grid such that the Shannon information rate across the input-output boundaries of the grid is maximized. Besides its importance in information theory, information rate has been proven to be a useful concept for principal as well independent component analysis (PCA, ICA). In contrast to linear fully connected neural networks, resistive grids due to their local coupling can resemble models of physical media and are feasible for a VLSI implementation. Results for spatially invariant as well as for the spatially variant case are presented and their relation to principal subspace analysis (PSA) is outlined. Simulation results show the validity of the proposed results.

Item Type: Conference or Workshop Item
Erschienen: 2006
Creators: Koeppl, H.
Title: Information Rate Maximization over a Resistive Grid
Language: English
Abstract:

The work presents the first results of the authors research on adaptive cellular neural networks (CNN) based on a global information theoretic cost-function. It considers the simplest case of optimizing a resistive grid such that the Shannon information rate across the input-output boundaries of the grid is maximized. Besides its importance in information theory, information rate has been proven to be a useful concept for principal as well independent component analysis (PCA, ICA). In contrast to linear fully connected neural networks, resistive grids due to their local coupling can resemble models of physical media and are feasible for a VLSI implementation. Results for spatially invariant as well as for the spatially variant case are presented and their relation to principal subspace analysis (PSA) is outlined. Simulation results show the validity of the proposed results.

Place of Publication: Vancouver, BC
Publisher: IEEE
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Event Title: The 2006 IEEE International Joint Conference on Neural Network Proceedings
Date Deposited: 04 Apr 2014 12:42
Official URL: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...
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