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Bishop et al.'s goal in introducing generative topographic mapping was not biological plausibility.

Generative Topographic Mapping produces PDFs for latent variables given data points.

GTM was developed in part to be a good alternative for the SOM algorithm.

GTM, at least in its original formulation, is a batch algorithm.

GTM uses the EM algorithm to fit adaptive parameters $\mathbf{W}$ and $\beta$ of a constrained mixture of Gaussian model to the data.

The constrained mixture of Gaussian model consists of a set $\{\mathbf{x}_i\}$ of points in latent space which are mapped via a general linear model $\mathbf{W}\phi(x)$ into data space, and the inverse variance $\beta$ of the Gaussian noise model.