What if artificial intelligence didn’t start with machines — but with microscopes?
Long before artificial intelligence became a buzzword of 21st century, the human brain was the original blueprint for intelligent systems. Instead of computers, it was actually biology – specifically, the intricate, beautiful network of neurones that comprise our nervous system was the source of the notion that machines could “think.”
The intriguing path from examining brain cells under a microscope to developing artificial neural networks, the cornerstone of contemporary artificial intelligence is examined in this blog. Discover how biological discoveries sparked the current deep learning revolution, meet trailblazing scientists like Golgi, Cajal, and Waldeyer-Hartz, and take part in discussions like the Neurone Doctrine versus the Reticular Theory.
Whether you’re an AI enthusiast, a neuroscience nerd, or just curious about where it all began. you’re in the right place.
The Reticular Theory Takes the Stage (1871–1873)
This was the time when people were trying to understand, what our nervous system looks like. Joseph von Gerlach a German professor, proposed that the nervous system is a single continuous network. His theory got more strength, around 1870’s when another famous Italian pathologist Camillo Golgi Invented a chemical reaction technique named The Staining Technique, which helped him to examine nervous tissue better. He used this technique to understand the tissue structure of the nervous system and also came up with the same conclusion as Joseph von Gerlach. This school of belief was later called The Reticular Theory.
The Rise of the Neuron Doctrine (1888–1891)
Santiago Ramón y Cajal a Spanish neuroscientist, Used the same Staining Technique but formulated an opposite theory stating, that the nervous system is a collection of discrete individual cells. Around 1891 a German neuroanatomist Heinrich Wilhelm Gottfried von Waldeyer-Hartz published a highly influential article about the works of several scientists including Dr. Cajal. He endorsed his theory of the nervous system being a collection of cells. In this process, he also coined the term Neuron for the first time. Soon this term started to emerge in the scientific literature and gradually Cajal’s theory later became known as Neuron Doctrine.
By the way if you are thinking what's wrong with Dr. Wilhelm's name.
Heinrich Wilhelm Gottfried von Waldeyer-Hartz
His actual given name was Heinrich Wilhelm Gottfried.
Von on the other hand was nobility prefix, it literally means “of” or “from”
Waldeyer was his family surname
Hartz was added later to his name. It refers to the Harz Mountains region in Germany.
Adding such suffixes was a way to honor one’s origins, especially among nobility or scholars.
Nobel Prize for Golgi and Cajal (1906)
Golgi Created the Staining Technique, which for the first time allowed individual neurons to be seen and put his vote for Reticular Theory.
Cajal on the other hand used the same technique but formulated the Neuron Doctrine theory. Because of both their efforts although different schools of thought, it cleared our understanding of neuroscience.
The Nobel Prize in Physiology or Medicine 1906 was awarded jointly to Camillo Golgi and Santiago Ramón y Cajal “in recognition of their work on the structure of the nervous system”
Because of this, it created a huge debate around the globe, Rectular Theory v/s Neuron Doctrine.
The Neuron Doctrine Prevails (1950)
After a long-lasting debate, during 1950’s electron microscopy finally confirmed the Neuron Doctrine theory.
Confirmation of Neuron Doctrine also puts the foundation of Artificial Neural Networks (ANN)
The Great Brain Debate
Even before Rectular Theory v/s Neuron Doctrine, Around 1860s, There was another debate to understand, whether
1) brains have specified areas for specific tasks
2) or are distributed, involving many areas working together.
Even before Rectular Theory v/s Neuron Doctrine, Around 1860s, Both theories were backed by big-name scientists. Although we still don’t understand our brain completely. However, we came to an understanding that both theories were partially correct and partially wrong. With our modern technology, we can understand most of the complex processes are distributed across the brain, but some of the functions are more localized at some specific part of the brain.
Our Neural Network models, we also follow a similar structure where the functionality of the models is distributed, but some specific functions are more localized. For example, In many multi-lingual models, we may want to optimize the whole structure to process the input and create output, but for specific language, we may focus on some specific part of the model.