Warren Sturgis McCulloch (November 16, 1898 – September 24, 1969) was an American neuropsychologist and cybernetician known for his work on the foundation for certain brain theories and his contribution to the cybernetics movement.
[1] Along with Walter Pitts, McCulloch created computational models based on mathematical algorithms called threshold logic which split the inquiry into two distinct approaches, one approach focused on biological processes in the brain and the other focused on the application of neural networks to artificial intelligence.
His brother was a chemical engineer and Warren was originally planning to join the Christian ministry.
As a teenager he was associated with the theologians Henry Sloane Coffin, Harry Emerson Fosdick, Herman Karl Wilhelm Kumm and Julian F. Hecker.
[3] He attended Haverford College then studied philosophy and psychology at Yale University, where he received a Bachelor of Arts degree in 1921.
He continued to study psychology at Columbia and received a Master of Arts degree in 1923.
Then he worked under Eilhard von Domarus at the Rockland State Hospital for the Insane.
In addition to his scientific contributions he wrote poetry (sonnets), and he designed and engineered buildings and a dam at his farm in Old Lyme, Connecticut.
He is remembered for his work with Joannes Gregorius Dusser de Barenne from Yale[7] and later with Walter Pitts from the University of Chicago.
He provided the foundation for certain brain theories in a number of classic papers, including "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943) and "How We Know Universals: The Perception of Auditory and Visual Forms" (1947), both published in the Bulletin of Mathematical Biophysics.
These, greatly due to the diversity of the backgrounds of the participants McCulloch brought in, became the foundation for the field.
In Wiener's Cybernetics (1948), he recounted an event in the spring of 1947, when McCulloch designed a machine to allow the blind to read, by converting printed letters to tones.
Gerhardt von Bonin saw the design, and immediately asked, " Is this a diagram of the fourth layer of the visual cortex of the brain?".
[8]: 22, 140 In his last days in 1960s, he worked on loops, oscillations and triadic relations with Moreno-Díaz; the reticular formation with Kilmer and dynamic models of memory with Da Fonseca.
[11] Bailey, Bonin, and McCulloch conducted a series of studies in the 1940s that identified connections in the brains of macaque and chimpanzee that are consistent with modern understanding of VOF.
[3] In the 1943 paper, they described how memories can be formed by a neural network with loops in it, or alterable synapses.
[14] He worked with Manuel Blum in studying how a neural network can be "logically stable", that is, can implement a boolean function even if the activation thresholds of individual neurons are varied.
[18][19] In the 1943 paper McCulloch and Pitts attempted to demonstrate that a Turing machine program could be implemented in a finite network of formal neurons (in the event, the Turing Machine contains their model of the brain, but the converse is not true[20]), that the neuron was the base logic unit of the brain.
In the 1947 paper they offered approaches to designing "nervous nets" to recognize visual inputs despite changes in orientation or size.
His team examined the visual system of the frog in consideration of McCulloch's 1947 paper, discovering that the eye provides the brain with information that is already, to a degree, organized and interpreted, instead of simply transmitting an image.
neurons, there exists a binary input for this universal network such that it exhibits the same pattern.
[25] He posited the concept of "poker chip" reticular formations as to how the brain deals with contradictory information in a democratic, somatotopical neural network.
Specifically, how the brain can commit the animal to a single course of action when the situation is ambiguous.
They designed a prototypic example neural network "RETIC", with "12 anastomatically coupled modules stacked in columnar array", which can switch between unambiguous stable modes based on ambiguous inputs.