Abstract
A unifying framework for analyzing models of neural map formation is presented based on growth rules derived from objective functions and normalization rules derived from constraint functions. Coordinate transformations play an important role in deriving various rules from the same function. Ten different models from the literature are classified within the objective function framework presented here. Though models may look different, they may actually be equivalent in terms of their stable solutions. The techniques used in this analysis may also be useful in investigating other types of neural dynamics.
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© 1997 Springer-Verlag Berlin Heidelberg
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Wiskott, L., Sejnowski, T. (1997). Objective functions for neural map formation. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020163
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DOI: https://doi.org/10.1007/BFb0020163
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