Developing Prognosis Tools to Identify Learning Difficulties in Children Using Machine Learning Technologies.

The Mental Attributes Profiling System was developed in 2002 (Laouris and Makris, Proceedings of multilingual & cross-cultural perspectives on Dyslexia, Omni Shoreham Hotel, Washington, D.C, 2002), to provide a multimodal evaluation of the learning potential and abilities of young children's brains. The method is based on the assessment of non-verbal abilities using video-like interfaces and was compared to more established methodologies in (Papadopoulos, Laouris, Makris, Proceedings of IDA 54th annual conference, San Diego, 2003), such as the Wechsler Intelligence Scale for Children (Watkins et al., Psychol Sch 34(4):309-319, 1997). To do so, various tests have been applied to a population of 134 children aged 7-12 years old. This paper addresses the issue of identifying a minimal set of variables that are able to accurately predict the learning abilities of a given child. The use of Machine Learning technologies to do this provides the advantage of making no prior assumptions about the nature of the data and eliminating natural bias associated with data processing carried out by humans. Kohonen's Self Organising Maps (Kohonen, Biol Cybern 43:59-69, 1982) algorithm is able to split a population into groups based on large and complex sets of observations. Once the population is split, the individual groups can then be probed for their defining characteristics providing insight into the rationale of the split. The characteristics identified form the basis of classification systems that are able to accurately predict which group an individual will belong to, using only a small subset of the tests available. The specifics of this methodology are detailed herein, and the resulting classification systems provide an effective tool to prognose the learning abilities of new subjects.

Does motoneuron adaptation contribute to muscle fatigue?

To help reduce the gap between the cellular physiology of motoneurons (MNs) as studied "bottom-up" in animal preparations and the "top-down" study of the firing patterns of human motor units (MUs), this article addresses the question of whether motoneuron adaptation contributes to muscle fatigue. Findings are reviewed on the intracellularly recorded electrophysiology of spinal MNs as studied in vivo and in vitro using animal preparations, and the extracellularly recorded discharge of MUs as studied in conscious humans. The latter "top-down" approach, combined with kinetic measurements, has provided most of what is currently known about the neurobiology of muscle fatigue, including its task and context dependencies. It is argued that although the question addressed is still open, it should now be possible to design new "bottom-up" research paradigms using animal preparations that take advantage of what has been learned with the use of relatively noninvasive quantitative procedures in conscious humans.