Genetic determinism: how not to interpret behavioral genetics

Recently, investigators in behavioral genetics have found loci on the genome (so-called ‘quantitative trait loci’ or QTLs) that are associated with complex mental traits, such as anxiety or novelty seeking. The interpretation of these findings raises interesting theoretical questions. At first sight, the discovery of ‘genes-for-personality’ seems to support genetic determinism and reductionism. Genetic determinism is the view that the phenotype is precoded in or determined by the genotype. However, evidence from developmental biology and neural modeling indicates that development is a result of interactive processes at many levels, not only the genome, so that geneticism must be rejected. Identifying QTLs and perhaps also the causal paths in the tangle of top-down and bottom-up influences between genome, organism and environment is best seen as a simplification. It amounts to considerably less than reduction in the classical sense of replacement via bridge laws or elimination. It is argued that higher (psychological and physiological) levels are functionally characterized and are irreducible to molecular-genetic levels. Therefore, it is to be expected that ideas about inter-level relations may be useful in clarifying the relation between loci on the genome (QTLs), gene products, the nervous system, behavior and personality, and to help identify the contribution of genetic factors in behavioral genetics. © 2000, Sage Publications. All rights reserved

Recurrent Neural Networks as Local Models for Time Series Prediction

International audience"Local models" (Walter, J., et al. International Joint Conference on Neural Networks, vol. 1. (1990) 589-594), consists on dividing the data into homogeneous clusters by Vector Quantization (VQ (Gray, R. M., and Neuhoff, D.L. IEEE Trans. Inf. Theory 44(6) (1998) 2325-2383)) to simplify the prediction task on each cluster and mostly inspired from the Self-Organizing Maps algorithm (SOM (Kohonen, T. Self-Organization and associative memory, 3rd edn. (1989))). Since recurrent neural networks have demonstrated in many times a better results and specially for chaotic time series (Boné, R. Recurrent Neural Networks for Time Series Forecasting. (2000)), we propose in this paper a method to use the Recurrent Neural Networks in the local approach