The artificial mind in 0 and 1: Warren McCulloch, the father of neural networks
- Mobilint Admin
- Apr 22
- 3 min read
Did you know? The first paper to lay the foundation of the modern neural network neurons made zero references to papers on the biology of real neurons.
It was in the 1940s, long before the concept of “deep learning” entered the mainstream, when the American neurophysiologist Warren McCulloch laid the mathematical groundwork for neural computation. His ideas not only shaped the architecture of artificial neural networks but also influenced how we build and think about intelligent systems today.
The “father of neural networks” had one ultimate goal in mind: understanding the human brain. In this post, we take a moment to honor the figure who helped establish the foundations of modern AI.
Who is Warren McCulloch, and why is he called “the father of neural network”?
Warren Sturgis McCulloch was an American neurophysiologist, psychiatrist, computer scientist, philosopher, and poet (he had a passion for sonnets). He is noted as an original and eccentric thinker far ahead of his time, and his works have influenced the developments in both artificial intelligence and neuroscience.
In 1919, he began exploring the field of logic. By 1923, he attempted to devise a logic for transitive verbs. Later in 1927, he received his MD and soon began his career in psychiatry at the Rockland State Hospital for the Insane. It was at the hospital when he began to understand the logical difficulties present in mental disorders like schizophrenia.
He believed that traditional observation alone couldn’t explain cognition and sought a new approach that combined biology with logic and engineering.
Walter Pitts: A prodigy and partner
In 1941, McCulloch met Walter Pitts, a mathematical prodigy with a deep understanding of logic. Pitts was also an eccentric genius who, as a teenager, ran away from home to attend college lectures.
Their shared interest in modeling the brain as a logical system led to a collaboration that would redefine how researchers conceptualize cognition. Together, they asked: If neurons are the basic units of the brain, can we mathematically model how they process information?
The McCulloch-Pitts neuron is born
In 1943, they introduced the McCulloch-Pitts (MCP) neuron, the first formal model of an artificial neuron. The MCP was a simple yet powerful model of how neurons might work using logic and math.
The MCP model considered neurons as binary threshold units:
If the sum of weighted inputs exceeds a fixed threshold, the neuron activates (outputs 1).
If not, it remains inactive (outputs 0).
Some additional assumptions included:
Activation requires simultaneous input signals.
Signal transmission includes a fixed time delay.
Any inhibitory input forces the neuron to remain inactive.
Network structure is static (no learning mechanism).
Their idea was that a network of these logical neurons could do anything a computer can do, things like math or decision-making. They were among the first to suggest that the brain might be like a computer, and their work helped inspire early computer designs, like the EDVAC by John von Neumann.
While the MCP model was ultimately too simplistic to explain the full biological complexity of the brain (leading Pitts to destroy some of his later, unpublished work), it laid the groundwork for what would become modern neural networks, mind-blowing as they are today.
A leading figure in cybernetics
McCulloch was also a key figure in the cybernetics movement. Its central theme is feedback loop, where a system’s output is used as its own input, influencing the next output and so on. This study introduced control theory and feedback systems to biology and engineering, which are concepts that still underpin intelligent systems today.
As one of the founding members of the American Society for Cybernetics and worked with Norbert Wiener, who is considered the originator of cybernetics as a scientific discipline.
How his legacy lives on
Though initially theoretical, McCulloch’s work gained influence in the 1950s and 60s, laying the intellectual foundation for artificial intelligence, cognitive science, and neuromorphic engineering.
McCulloch's abstract neuron may have lacked biological nuance, but it captured something deeper: the essence of computation in intelligence. In a field driven by acceleration and abstraction, the logic that powered the MCP model continues to echo in every neural network deployed at scale.
Wrapping up…
In this blog post, we explored the early research that laid the foundation for neural network “neurons” and the great mind behind it. For today’s machine learning engineers and AI architects, McCulloch’s work is more than just history; understanding where we came from is key to figuring out where we’re headed next.
At Mobilint, we design the "brain,” or the AI hardware for these artificial neurons, that turn bold ideas into reality with low power and high performance. Stay tuned to our blog for more behind-the-scenes looks at the minds and machines shaping intelligent computing.