Abstract
The 1943 McCulloch and Pitts seminal paper on artificial neurons (ANs) provided the stimulus for early neural network research. However, McCulloch's work in the 50's has been mainly overlooked. The importance of this later work is shown in this paper not only because McCulloch's neuron design was significantly more powerful than either the Perception or the Adeline, but also because of its ability to satisfy the XOR function 12 years before the publication of Minsky and Papert's book, Perceptrons. The similarities between McCulloch's work in the 50's and 60's and more current AN research are also identified here. Some rudimentary experiments with AN design using a delta learning rule are also included. It is hoped that by recasting McCulloch's and the author's early work in terms of current technology, further research will be stimulated in designing efficient artificial neural systems that provide the stability and reliability of biological systems. These were the goals of McCulloch's original work.
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W.S. McCulloch and W. Pitts, “A Logical Calculus of the Ideas Immanent in Neural Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, 1943.
F. Rosenblatt, “The Perceptron: a Probabalistic Model for Information Storage and Organization in the Brain,” Psychological Review, 65, pp. 386–408, 1958.
M. Minsky and S. Papert, Perceptrons, MIT Press, Cambridge, MA, 1969.
R. Eberhart and R. Dobbins, Ed., Neural Network PC Tools, Academic Press, San Diego, CA, 1990.
M. Caudill and C. Butler, Naturally Intelligent Systems, MIT Press, Cambridge, MA, 1990.
W. Altman, Apprentices of Wonder, Bantam Books, New York, 1989.
W. McCulloch, “The Stability of Biological Systems,” Homeostatic Mechanisms, Brookhaven Symposia in Biology: No. 10, Uptown, NY, 1957.
J.E. Whitesitt, Boolean Algebra and its Applications, p. 33, Addison-Wesley Publishing Co., Reading, MA, 1961.
M. Minsky, Computation: Finite and Infinite Machines, Prentice-Hall, Inc., Englewood Cliffs, NJ, 1967.
N. Scott, Analog and Digital Computer Technology, McGraw-Hill Co., New York, 1960.
W. McCulloch, “What is a Number That a Man May Know it and a Man, That he May Know a Number?,” in W. McCulloch, Embodiments of Mind, MIT Press, Cambridge, MA, 1988.
R. Scott, “Construction of a Neuron Model,” IRE Transactions on Bio-Medical Electronics, Vol. BME 8, No.3, July, 1961.
N. Nilsson, The Mathematical Foundations of Learning Machines, Morgan Kaufman Publishers, San Mateo, CA, 1990.
R. Duda and P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
D. Rumelhart, G. Hinton, and R. Williams, “Learning Internal Representations by Error Propagation,” in D. Rumelhart and J. McClelland, Eds., Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, MIT Press, Cambridge, MA, 1986.
T. Sejnowski, “Higher Ordered Boltzman Machines,” American Institute of Physics Conference Proceedings, No. 151, Neural Networks for Computing, pp. 398–403, Snowbird, UT, 1986.
M Klassen and Y. Pao, “Characteristics of the Functional-Link Net: A Higher Order Delta Rule Net,” IEEE Proceedings of the 2nd Annual International Conference on Neural Networks, San Diego, CA, June, 1988.
Y. Pao, Adaptive Pattern Recognition and Neural Networks, Addison-Wesley Publishing Co., Reading, MA, 1989.
R. Goodman, C. Higgins, J. Miller, and P. Smyth, “Rule-Based Neural Networks for Classification and Probability Estimation,” Neural Computation, pp. 781–804, Vol. 4, No. 6, Nov, 1992.
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© 1993 Springer-Verlag Berlin Heidelberg
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Scott, R.J. (1993). McCulloch's neurons revisited. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_125
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DOI: https://doi.org/10.1007/3-540-56798-4_125
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