Analysis of Linsker's application of Hebbian rules to linear networks

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Deep learning in neural networks: An overview (2014, neutral): “…Amari, Cichocki, & Yang, 1996; Barlowet al., 1989;Dayan&Zemel, 1995;Deco&Parra, 1997; Field, 1994; Hinton, Dayan, Frey, & Neal, 1995; Linsker, 1988; MacKay & Miller, 1990; Plumbley, 1991; Redlich, 1993; Schmidhuber, 1992b, 1992c; Schraudolph & Sejnowski, 1993; Zemel, 1993; Zemel & Hinton, 1994).”
The Role of Constraints in Hebbian Learning (1994, methodology): “Consider rst a simple equation for Hebbian synaptic plasticity, d dt w=Cw, where C is a matrix describing correlations among input activities (derived for example in MacKay and Miller, 1990a).”
Analysis of Linsker's Simulations of Hebbian Rules (1990, background): “Several of the results are more general, applying to any covariance matrix Q. Space constrains us to postpone general discussion, technical details, and discussion of other model networks, to a future publication ( MacKay and Miller 1990 ).,... of saturation constraints, 2p will always dominate the ”