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Models of Neural Development

Lupe

Topographic projections between neural sheets, orientation columns and ocular dominance columns in early visual areas have served as paradigmatic model systems for understanding the mechanisms underlying neural plasticity and development. Using mathematical models and computer simulations we investigated how activity driven and intrinsic processes interact in order to generate the observed anatomical connectivity patterns and response properties of neurons. We describe the development of those patterns as a goal-oriented (in the sense of underlying cost-functions) self-organizing process, which extracts information from the environment and imprints this knowledge into neural circuits. Particular emphasis was given to competitive networks including the Self-Organizing Map, which are known to trade smoothness vs. completeness of representations and which lead to patterns which fit experimental data surprisingly well.The mathematical properties of self-organizing maps were also analysed in a machine learning context. For details see "Research" page "Learning Vector Quantization and Self-organizing Maps"

Acknowledgements: Research was funded by BMBF, DFG, and the Technische Universität Berlin.

Selected Publications:

Development and Regeneration of the Retinotectal Map in Goldfish: A Computational Study
Citation key Weber1997
Author Weber, C. and Ritter, H. and Cowan, J. and Obermayer, K.
Pages 1603 – 1623
Year 1997
DOI 10.1098/rstb.1997.0144
Journal Philosophical Transaction of the Royal Society B
Volume 352
Abstract We present a simple computational model to study the in terplay of activity dependent and intrinsic processes thought to be involved in the formation of topographic neural projections. Our model consists of two input layers which project to one target layer. The connections between layers are described by a set of synaptic weights. These weights develop according to three interacting developmental rules: (i) an in trinsic fiber-target interaction which generates chemospecific adhesion between afferent fibers and target cells, (ii) an in trinsic fiber-fiber interaction which generates mutual selective adhesion between the afferent fibers and (iii) an activity-dependent fiber-fiber interaction which implements Hebbian learning. Additionally, constraints are imposed to keep synaptic weights finite. The model is applied to a set of eleven experiments on the regeneration of the retinotectal projection in goldfish. We find that the model is able to reproduce the outcome of an unprecedented range of experiments with the same set of model parameters, including details of the size of receptive and projective fields. We expect this mathematical framework to be a useful tool for the analysis of developmental processes in general.
Bibtex Type of Publication Selected:development
Link to publication Link to original publication Download Bibtex entry

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