Perceptron
Rosenblatt described a trainable recognition model.
Why it matters
It made learning from examples a concrete research programme.
The historical perceptron differs substantially from today’s deep networks.
Research · November 1958
Placed by the contemporary primary publication. The exact event date is not known; its documented interval appears below.
Event record
- Event date
- 1958
- Timeline date
- Primary publication date
- Verification
- Sources gathered automatically · September 11, 2026
- Lines
- ID
- evt-0003
Sources
- primary The Perceptron
Psychological Review · Published November 1958
Records that link to this one
- Related ADALINE and the least-mean-squares rule
Both train a linear unit from examples; ADALINE uses a different weight-update rule.
- Contrasts Perceptrons: the limits of a single layer
The book formally analyses which problems a single-layer perceptron cannot solve.
Perceptrons: An Introduction to Computational Geometry - Related McCulloch and Pitts neuron model
The perceptron added a learning procedure to the formal neuron.
- Extends Principles of Neurodynamics
The book develops the model first published in 1958.
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms - Extends The perceptron convergence proof
The proof concerns the perceptron learning algorithm itself.
On Convergence Proofs for Perceptrons - Builds on Glushkov: Introduction to Cybernetics
The book's chapter on training pattern recognition sets out a theory of discrete alpha-perceptrons and cites Rosenblatt's perceptron.
Introduction to Cybernetics, by V. M. Glushkov (FTD-TT-65-942/1+2, unedited rough draft translation) - Builds on Mark I Perceptron: the perceptron in hardware
The Mark I, by its engineers' article, was built to verify the mathematical predictions of Rosenblatt's perceptron.
The Mark I Perceptron (John C. Hay, Charles W. Wightman), Research Trends (Cornell Aeronautical Laboratory) VIII(1), 1-4, Spring 1960 - Builds on WISARD: a neural network made of memory chips
Aleksander places WISARD in the line of neural modelling that includes Rosenblatt's perceptron, and calls it a single-layer neural net whose neurons are memory chips.
Memory Networks for Practical Vision Systems: Design Calculations (I. Aleksander), chapter 11 of Artificial Vision for Robots, ed. I. Aleksander, 197-214, 1983: publisher's two-page preview - Contrasts Mead names neuromorphic systems
The perceptron took the idea of learning from the nervous system; Mead proposes taking its physics of computation: analog circuits that perform the operations neurons do with charge and current.
Neuromorphic Electronic Systems (Carver Mead), Proceedings of the IEEE 78(10), 1629-1636, October 1990