Abstract
This chapter reviews and examines a variant type of computational unit which we have recently proposed for use in multi-layer neural networks [3]. Instead of the output of this unit depending on a weighted sum of the inputs, it depends on a weighted product. In justifying the introduction of a new type of unit we explore at some length the rationale behind the use of multi-layer neural networks, and the properties of the computational units within them. At the end of the chapter we discuss a biological model for a single complex neve cell with active dendritic membrane that uses the product units.
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Durbin, R., Rumelhart, D.E. (1990). Product Units with Trainable Exponents and Multi-Layer Networks. In: Soulié, F.F., Hérault, J. (eds) Neurocomputing. NATO ASI Series, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76153-9_2
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DOI: https://doi.org/10.1007/978-3-642-76153-9_2
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