Computer Analysis and Modeling of Biological Signals and Systems
Neural Nets 1: Some Readings and Links
"Receptive fields", from Sherrington (1906) to Hartline (1938) and Hubel & Wiesel (1959).
K.S. Lashley, "Basic Neural Mechanisms in Behavior", Psychological Review, 1930.
McCulloch and Pitts' neural logical calculus
Warren McCulloch and Walter Pitts, "A logical calculus of the ideas immanent in nervous activity",Bulletin of Mathematical Biophysics, 1943.
Amanda Gefter, "The Man Who Tried to Redeem the World with Logic", Nautilus, 2015.
Donald Hebb, "The Organization of Behavior", 1949. [See also the Wikipedia page...]
F. Rosenblatt, "The Perceptron: A probabilistic model for information storage and organization", Psych. Review 1958.
Minsky and Papert on the Perceptron
Marvin Minsky and Seymour Papert, Perceptrons: An introduction to computational geometry (originally published 1969)
Classifying multivariate data: Discriminant analysis vs. the Perceptron
K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position", Biological Cybernetics,1980.
J.J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", PNAS 1982 [See also the Wikipedia page...]
David Rumelhart and James McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986)
G. Tesauro, "Simple neural models of classical conditioning", Biological Cybernetics 1986. [See also Wikipedia on Classical Conditioning...]
Andrey Karpathy, "The Unreasonable Efectiveness of Recurrent Neural Networks", 2015. [See also Wigner's 1960 paper, and the Wikipedia article...]
Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, 2016.
Eve Armstrong, "A Neural Networks Approach to Predicting How Things Might Have Turned Out Had I Mustered the Nerve to Ask Barry Cottonfield to the Junior Prom Back in 1997", 4/1/2017.
A. Vaswani et al., "Attention is all you need", NIPS 2017.
Joel Hestness et al., "Deep learning scaling is predictable, empirically", 12/1/2017.
Matthew Peters et al., "Deep contextualized word representations", 3/22/2018 [ELMo].
Alec Radford,"Improving Language Understanding with Unsupervised Learning, 6/11/2018" [GPT].
Jacob Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", 11/10/2018.
Noah Smith, "Contextual Word Representations: A Contextual Introduction", 2/2019.
Ashley Pilipsiszyn, "Better Language Models and Their Implications", 2/14/2019 [GPT-2].
D. Ma et al., "Probing Acoustic Representations for Phonetic Properties", IEEE ICASSP 2021.
M. Bartelds et al., "Neural Representations for Modeling Variation in English Speech", J. Phonetics 2022.
Gerardo Adesso, "Towards The Ultimate Brain: Exploring Scientific Discovery with ChatGPT AI", 2023. [Speculative fragments from ChatGPT...]
Matlab's Deep Learning Toolbox.