Learning with the perceptron

A trainable model connected adaptive weights with pattern recognition.

Frank Rosenblatt

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The contribution

Rosenblatt described a probabilistic model of perception and learning. The perceptron became an influential example of a system that could adjust its behavior from examples, giving neural learning a concrete mathematical and experimental direction.

What this does not establish

The capabilities of a single-layer perceptron are limited. This entry does not treat it as equivalent to a modern multilayer network.

Why this date?

1958 dates this journal paper. Earlier perceptron reports exist, so this is not an invention-date claim.

This entry follows the linked publication. Read the source and date conventions.

The original work

The perceptron: A probabilistic model for information storage and organization in the brain

Authors: Frank Rosenblatt
Publication: Psychological Review, 65 (6), 386–408
Source type: Journal article

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Cited By

Atlas entries whose reference lists cite this work or the publication version noted below.

Associative Memory: A System-Theoretical Approach (1977)
Reference 137 identifies Rosenblatt's 1958 Psychological Review paper. Check the reference list.
The Utility Driven Dynamic Error Propagation Network (1987)
Listed in the source bibliography. Check the reference list.

Citations

Works in the Atlas cited by this entry.

A logical calculus of the ideas immanent in nervous activity (1943)
Listed in the source bibliography. Check the reference list.
The Organization of Behavior: A Neuropsychological Theory (1949)
Reference 7 cites the 1949 book. Check the reference list.

Bibliography coverage: Reference section indexed. Open the source.

Raw extraction retains OCR errors, ligatures, page headers and column-order artifacts. Entries have not all been normalized or individually verified.

Read the indexed bibliography

Reference section 1 (PDF pages 23)

1. ASHBY, W. R. Design for a brain. New York: Wiley, 1952. 2. CULBERTSON, J. T. Consciousness and behavior. Dubuque, Iowa: Wm. C. Brown, 1950. 3. CULBERTSON, J. T. Some uneconomical robots. In C. E. Shannon & J. McCarthy (Eds.), Automata studies. Princeton: Princeton Univer. Press, 1956. Pp. 99-116. 4. ECCLES, J. C. The neurophysiological basis of mind. Oxford: Clarendon, 1953. 5. GOLDSTEIN, K. Human nature in the light of psychopathology. Cambridge: Harvard Univer. Press, 1940. 6. HAYEK, F. A. The sensory order. Chicago: Univer. Chicago Press, 1952. 7. HEBB, D. O. The organization of behavior. New York: Wiley, 1949. 8. KLEENE, S. C. Representation of events in nerve nets and finite automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies. Princeton: Princeton Univer. Press, 1956. Pp. 3-41. 9. KOHLER, W. Relational determination in perception. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior. New York: Wiley, 1951. Pp. 200-243. 10. McCuLLOCH, W. S. Why the mind is in the head. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior. New York: Wiley, 1951. Pp. 42-111. 11. MCCULLOCH, W. S., & PITTS, W. A logical calculus of the ideas immanent in nervous activity. Butt. math. Biophysics, 1943, S, 115-133. 12. MILNER, P. M. The cell assembly: Mark II. Psychol. Rev., 1957,64,242252. 13. MINSKY, M. L. Some universal elements for finite automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies. Princeton: Princeton Univer. Press, 1956. Pp. 117-128. 14. RASHEVSKY, N. Mathematical biophysics. Chicago: Univer. Chicago Press, 1938. 15. ROSENBLATT, F. The perceptron: A theory of statistical separability in cognitive systems. Buffalo: Cornell Aeronautical Laboratory, Inc. Rep. No. VG-1196-G-1, 1958. 16. UTTLEY, A. M. Conditional probability machines and conditioned reflexes. In C. E. Shannon & J. McCarthy (Eds.), Automata studies. Princeton: Princeton Univer. Press, 1956. Pp. 253-275. 17. VON NEUMANN, J. The general and logical theory of automata. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior. New York: Wiley, 1951. Pp. 1-41. 18. VON NEUMANN, J. Probabilistic logics and the synthesis of reliable organisms from unreliable components. In C. E. Shannon & J. McCarthy (Eds.), Automata studies. Princeton: Princeton Univer. Press, 1956. Pp. 43-98.

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