What is the origin of the autoencoder neural networks? I searched on Google, Wikipedia, Google scholar, and more, but I could not find the origin of Autoencoders. Perhaps it's one of those concepts that evolved very gradually, and it's impossible to trace back a clear starting point, but still I would like to find some kind of summary of the main steps of their development.
The chapter about autoencoders in Ian Goodfellow, Yoshua Bengio and Aaron Courville's Deep Learning book says:

The idea of autoencoders has been part of the historical landscape of
  neuralnetworks for decades (LeCun, 1987; Bourlard and Kamp, 1988;
  Hinton and Zemel,1994). Traditionally, autoencoders were used for
  dimensionality reduction or feature learning.

This presentation by Pascal Vincent says:

Denoising using classical autoencoders was actually introduced much
  earlier (LeCun, 1987; Gallinari et al., 1987), as an alternative to
  Hopfield networks (Hopfield, 1982).

This seems to imply that "classical autoencoders" existed before that: LeCun and Gallinari used them but did not invent them. I see no trace of "classical autoencoders" earlier than 1987.
Any ideas?
 A: According to the history provided in Schmidhuber, "Deep learning in neural networks: an overview," Neural Networks (2015), auto-encoders were proposed as a method for unsupervised pre-training in Ballard, "Modular learning in neural networks," Proceedings AAAI (1987). It's not clear if that's the first time auto-encoders were used, however; it's just the first time that they were used for the purpose of pre-training ANNs.
As the introduction to the Schmidhuber article makes clear, it's somewhat difficult to attribute all of the ideas used in ANNs because the literature is diverse and terminology has evolved over time.
A: The paper below talks about autoencoder indirectly and dates back to 1986.(which is a year earlier than the paper by Ballard in 1987)
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning internal representations by error propagation." , Parallel Distributed Processing. Vol 1: Foundations. MIT Press, Cambridge, MA, 1986. 
The paper basically describes a novel kind of feedforward network at that time , and its mathematical formalism.
A: Reviving this thread - In "Neurocomputing" by Robert Hecht-Nielsen @ 1990 there is reference to a 1986 paper by Cottrell/Munro/Zipser that outlines use of a neural network that has the architecture of an autoencoder, and is trained on the identity function, for compression and reconstruction of image data.  The term "autoencoder" isn't mentioned, but that's what it is.  There is no mention of using it for anomaly detection either.  Seems like that usage  was discovered later.
A: The first clear autoencoder presentation featuring a feedforward, multilayer neural network with a bottleneck layer was presented by Kramer in 1991 (full text at https://people.engr.tamu.edu/rgutier/web_courses/cpsc636_s10/kramer1991nonlinearPCA.pdf). He discusses dimensionality reduction and feature extraction and applications such as noise filtering, anomaly detection, and input estimation. Variational autoencoders, referred to as "robust autoassociative neural networks", were anticipated by by Kramer, 1992 (https://www.sciencedirect.com/science/article/abs/pii/009813549280051A?via%3Dihub). It wasn't until 15 years later that Hinton popularized autoencoders for dimensionality reduction, in 2006.
