Biomedical subjects

Renzo Perfetti

Publications and source records attributed to Renzo Perfetti.

4 recordsLinked to original sources

PubMed2006

Associative memory design for 256 gray-level images using a multilayer neural network.

A design procedure is presented for neural associative memories storing gray-scale images. It is an evolution of a previous work based on the decomposition of the image with 2L gray levels into L binary patterns, stored in L uncoupled neural networks. In this letter, an L-layer neural network is proposed with both intralayer and interlayer connections. The connections between different layers introduce interactions among all the neurons, increasing the recall performance with respect to the uncoupled case. In particular, the proposed network can store images with the commonly used number of 256 gray levels instead of 16, as in the previous approach.

Algorithms↗

PubMed2006

Analog neural network for support vector machine learning.

An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.

Computers, Analog↗

PubMed2006

Associative memory design using support vector machines.

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebb's law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples.

Algorithms↗

PubMed1997

Training Spatially Homogeneous Fully Recurrent Neural Networks in Eigenvalue Space.

A new design method for spatially-homogeneous, fully recurrent neural networks is presented. In our approach the eigenvalues of the synaptic matrix, rather than the weights, are learned from the examples. When the learning process is carried out, the connection weights are easily computed from the eigenvalues by inverse discrete Fourier transform. The adaptation is performed in the eigenvalue space in order to simply incorporate in the training algorithm the conditions for the uniqueness of the steady-state. As a consequence, the trained networks are insensitive to initial conditions. The method is illustrated by computer simulations concerning two specific feature extraction examples. Copyright 1996 Elsevier Science Ltd.

Journal Article↗