The capacity and attractor basins of associative memory models

The original publication is available at www.springerlink.com . Copyright SpringerThe performance characteristics of five variants of the Hopfield network are examined. Two performance metrics are used: memory capacity, and a measure of the size of basins of attraction. We find that the posttraining adjustment of processor thresholds has, at best, little or no effect on performance, and at worst a significant negative effect. The adoption of a local learning rule can, however, give rise to significant performance gains.Peer reviewe

High performance associative memory models and weight dilution

The consequences of diluting the weights of the standard Hopfield architecture associative memory model, trained using perceptron like learning rules, is examined. A proportion of the weights of the network are removed; this can be done in a symmetric and asymmetric way and both methods are investigated. This paper reports experimental investigations into the consequences of dilution in terms of: capacity, training times and size of basins of attraction. It is concluded that these networks maintain a reasonable performance at fairly high dilution rates.Final Accepted Versio

Global and Feature Based Gender Classification of Faces: A Comparison of Human Performance and Computational Models

Original paper can be found at: http://eproceedings.worldscinet.com/9789812701886/9789812701886_0036.html Copyright World Scientific Publishing Company. http://dx.doi.org/10.1142/9789812701886_0036Most computational models for gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here, we use a global and feature based representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone.Peer reviewe

High Performance Associative Memories and Structured Weight Dilution

Copyright SpringerThe consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance. It is concluded that these networks maintain a reasonable level of performance at fairly high dilution rates

The analysis of animate object motion using neural networks and snakes

This paper presents a mechanism for analysing the deformable shape of an object as it moves across the visual field. An object’s outline is detected using active contour models, and is then re-represented as shape, location and rotation invariant axis crossover vectors. These vectors are used as input for a feedforward backpropagation neural network, which provides a confidence value determining how ‘human’ the network considers the given shape to be. The network was trained using simulated human shapes as well as simulated non-human shapes, including dogs, horses and inanimate objects. The network was then tested on unseen objects of these classes, as well as on an unseen object class. Analysis of the network’s confidence values for a given animated object identifies small, individual variations between different objects of the same class, and large variations between object classes. Confidence values for a given object are periodic and parallel the paces being taken by the object

A neural network model of visual object recognition impairment after brain damage

Dysfunction of the visual object recognition system in humans is briefly discussed and a basic connectionist model of visual object recognition is introduced. Experimentation in which two variants of this model are lesioned is undertaken. The results suggest that the well documented phenomenon of superordinate preservation is model independent. Differential category specific recognition deficits are also observed in this model, however these are sensitive to each particular variant

Using Simple Neural Networks to Correct Errors in Optical Data Transmission.

We have demonstrated the applicability of neural-network-based systems to the problem of reducing the effects of signal distortion, and shown that such a system has the potential to reduce the bit-error-rate in the digitized version of the analogue electrical signal derived from an optical data stream by a substantial margin over existing techniques

The Role of Global and Feature Based Information in Gender Classification of Faces: A Comparison of Human Performance and Computational Models

Peer reviewe