BIMA showcases research at international conference
Tuesday 18 March 2008
The Department's research is being showcased again this year at the International Joint Conference on Neural Networks. The Biologically Inspired Modelling and Applications (BIMA) group has two papers accepted for this prestigious conference, with Dr Matthew Casey, Athanasios Pavlou and Yu Fu travelling to Hong Kong to present their work between the 1st and 6th June.
Casey, M.C. & Pavlou, A., A Behavioral Model of Sensory Alignment in the Superficial and Deep Layers of the Superior Colliculus
The ability to combine sensory information is an important attribute of the brain. Multisensory integration in natural systems suggests that a similar approach in artificial systems may be important. Multisensory integration is exemplified in mammals by the superior colliculus (SC), which combines visual, auditory and somatosensory stimuli to shift gaze. However, although we have a good understanding of the overall architecture of the SC, as yet we do not fully understand the process of integration. While a number of computational models of the SC have been developed, there has not been a larger scale implementation that can help determine how the senses are aligned and integrated across the superficial and deep layers of the SC. In this paper we describe a prototype implementation of the mammalian SC consisting of self-organizing maps linked by Hebbian connections, modeling visual and auditory processing in the superficial and deep layers. The model is trained on artificial auditory and visual stimuli, with testing demonstrating the formation of appropriate spatial representations, which compare well with biological data. Subsequently, we train the model on multisensory stimuli, testing to see if the unisensory maps can be combined. The results show the successful alignment of sensory maps to form a multisensory representation. We conclude that, while simple, the model lends itself to further exploration of integration, which may give insight into whether such modeling is of benefit computationally.
Fu, Y. & Browne, A., Investigating the Influence of Feature Correlations on Automatic Relevance Determination
Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper, we demonstrate that the predicted local relevance rank for the relevant features could be influenced by their highly correlated redundant features, according to the strength of their correlations.