Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Evolving Connectionist Models to capture population variability across language development: modelling children’s past tense formation

Children’s acquisition of English past tense has been widely studied as testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences. In this paper, we explore the use of populations of Artificial Neural Networks (ANNs) that evolve according to Behavioural Genetics principles in order to create computational models capable of capturing the population variability exhibited by children in acquiring English past tense verbs. Literature in the field of Behavioural Genetics views variability in children’s learning in terms of genetic and environmental influences. In our model, the effects of genetic influences are simulated through variations in parameters controlling computational properties of ANNs, and the effects of environmental influences are simulated via a filter applied to the training set. This filter alters the quality of information available to the artificial learning system and creates a unique subsample of the training set for each simulated individual. Our approach uses a population of twins to disentangle genetic and environmental influences on past tense performance and to capture the wide range of variability exhibited by children as they learn English past tenses. We use a novel technique to create the population of ANN-twins based on the biological processes of meiosis and fertilisation. This approach allows modelling of both individual differences and development (within the lifespan of an individual) in a single framework. Finally, our approach permits the application of selection on developmental performance on the quasi-regular task across generations. Setting individual differences within an evolutionary framework is an important and novel contribution of our work. We present an experimental evaluation of this model focusing on individual differences in performance. The experiments led to several novel findings including divergence of population attributes during selection to favour regular verbs, irregular verbs, or both; evidence of canalisation, analogous to Waddington’s developmental epigenetic landscape, once selection starts targeting a particular aspect of task domain; and limiting effect on the power of selection in the face of stochastic selection (roulette wheel), sexual reproduction, and a variable learning environment for each individual. Most notably, the heritability of traits showed an inverse relationship to optimisation. Selected traits show lower heritability as the genetic variance of the population reduces. The simulations demonstrate the viability of linking concepts such as heritability of individual differences, cognitive development, and selection over generations within a single computational framework