Learning by maximizing the information transfer through nonlinear noisy neurons and "noise breakdown"

By Schuster. 1992

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Deep learning in neural networks: An overview (2014, background): “…Belouchrani, Abed-Meraim, Cardoso, & Moulines, 1997; Cardoso, 1994; Comon, 1994; Hyvärinen, Karhunen, & Oja, 2001; Jutten & Herault, 1991; Karhunen & Joutsensalo, 1995; Molgedey & Schuster, 1994; Schuster, 1992; Shan & Cottrell, 2014; Shan, Zhang, & Cottrell, 2007; Szabó, Póczos, & Lőrincz, 2006).”
One Big Net For Everything (2018, background): “…of q special output units and train them through unsupervised learning [62] to produce for t ≤ t T a vector code ( t ) ∈ R q that represents an ideal factorial code [2] of the observed history so far, or that encodes the data in related ways generally considered useful, e.g., [23, 28, 81, 68, 21].”
Non linear neurons in the low noise limit : a factorial code maximizes information transferJean (1994, background): “Still, there are works on the optimization of the transfer function[5], on the study of input distributions to which a given transfer function is optimally adapted[21]; on the use of redundancy reduction for binary, more generally discrete, coding[22, 23]; on networks of binary neurons studied with ”