Technical Breakdown: The Perceptron
Tracing the Origins of Neural Networks: How the Perceptron Shaped Modern AI
Summary
Perceptron Foundation: Rosenblatt’s perceptron laid the groundwork for modern neural networks, influencing AI models like ChatGPT and DeepSeek.
Unsupervised Learning: The perceptron self-organized data into categories, anticipating unsupervised learning used in AI today.
Multimodal AI: Rosenblatt demonstrated the perceptron could process combined audio-visual inputs, foreshadowing modern multimodal models like GPT-4o.
Limitations & Future AI: The perceptron struggled with abstract relationships, highlighting the need for advanced architectures like Transformers.
Fundamental Questions
This paper written by Frank Rosenblatt in 1958 introduces the perceptron, the foundation for every neural network used in machine learning models today. ChatGPT, Deepseek, Claude, and every other machine learning model are built upon the discoveries of this paper.
Rosenblatt starts off the paper asking a couple key questions about the human brain:
In what form is information stored or remembered?
How does information contained in storage, or in memory, influence recognition and behaviour?
At the time, the two main beliefs for the first question were:
Coded Memory Theorists: Storage of sensory information is in the form of coded representations or images, with some sort of one-to-one mapping between the sensory stimulus (data) and the stored pattern.
Empiricist Tradition: Images of stimuli may never be recorded, instead, the central nervous system simply acts as an intricate switching network, where retention takes the form of new connections, or pathways, between centres of activity.
The alternate views for the second questions were:
Coded Memory Theorists: Recognition happens by comparing new sensory input to stored information to determine if it has been seen before and decide how to respond.
Empiricist Tradition: Since stored information forms new connections, new stimuli automatically use these pathways, triggering the right response without needing a separate recognition process.
Rosenblatt holds the empiricist tradition viewpoint, and thus, the perceptron is based off of this belief.
The Perceptron
The perceptron acts as a hypothetical nervous system that can dynamically respond to new inputs given to it. Previously, most publications thought of ways to produce a deterministic model rather than actually try to simulate the brain. To make it more like a brain, Rosenblatt opted to use probabilistic theory rather than symbolic logic (boolean; true or false).
The theory in which the perceptron is based upon can be summarized by the following five points:
The physical connections of the system are not identical between perceptrons, as they are randomly assigned during construction.
The original system of connected cells is capable of a certain amount of plasticity (change).
Through exposure to a large sample of stimuli, those which are most similar will tend to form pathways to the same area in the perceptron.
The application of a positive and/or negative reinforcement may facilitate or hinder whatever formation of connections is currently in progress.
Similarity in such a system is represented by a tendency of similar stimuli to activate the same sets of cells.
After listing these foundational theories of the perceptron, he goes on to say:
“The structure of the system (algorithm), as well as the ecology of the stimulus-environment (training data), will affect, and will largely determine, the classes of ‘things’ into which the perceptual world is divided.”
This quote showcases his early grasp of machine learning training philosophies—long before they became mainstream.
Organization of the Perceptron
In Rosenblatt’s study, he created the photo-perceptron (a perceptron based on learning patterns in images). The organization of such a perceptron can be found below:
Retina
The retina is where the perceptron first receives data, much like how our eyes capture images. It consists of a grid of sensors that detect simple features, such as areas of light and dark.
Each sensor (or neuron) in the retina converts what it detects into electrical signals. These signals are then passed forward to the next stage for further processing.
Projection Area
Once the signals leave the retina, they enter the projection area, where the information is refined. Here’s what happens:
Each retinal sensor is connected to this area through localized connections, meaning that only nearby sensors influence each processing unit.
This ensures that different parts of the image are processed independently before being combined later.
The threshold units in this layer act like switches—they only allow signals above a certain strength to pass through. Weak or irrelevant signals are filtered out.
This step ensures that only important visual features are sent to the next stage.
Association Area
The association area is where the perceptron starts detecting more complex relationships in the data. To illustrate, if one part of the projection area detects a vertical line and another detects a horizontal line, the association area might combine these signals to recognize a right-angle shape, like a corner.
Instead of having strictly localized connections, this layer introduces random connections that mix signals from different parts of the projection area.
Why is this randomness useful?
It allows the perceptron to discover patterns beyond just individual pixels or small features.
It helps the system generalize, making it capable of recognizing variations of the same object.
Like before, threshold units in this stage filter out weak signals. However, unlike earlier layers, this area has bidirectional connections, meaning signals can move back and forth to refine the interpretation before reaching the final stage.
Output Area
At the final stage, processed signals reach the response units (R₁, R₂, …, Rₙ). These units determine the perceptron’s final decision—whether it has recognized a particular shape, letter, or pattern.
The decision process works as follows:
If enough signals align in favour of a particular outcome, the corresponding response unit is activated.
This means the perceptron has "recognized" the stimulus and produces an output.
For the perceptron to learn, it must adjust its processing units or connections so that certain stimuli trigger stronger responses over time. Initially, signals may be random, but with reinforcement, similar inputs will produce more consistent reactions. This adaptation is what defines learning.
Key Discoveries
With this innovative paper, came innovative discoveries. The perceptron itself may be recognized as one of the most influential discoveries in the field of mathematics, computer science, and neuroscience. In testing the perceptron’s capabilities, Rosenblatt had some very interesting findings.
Unsupervised Learning
When testing on IBM’s 704 computer (made over 70 years ago) at Cornell’s Aeronautical Lab, Rosenblatt found a very interesting property of the perceptron.
“If the system is exposed to a random series of stimuli from two dissimilar classes, and all of its responses are automatically reinforced without any regard to whether they are right or wrong, … the perceptron will spontaneously recognize the difference between the two classes”.
The concept of allowing a machine learning model to independently identify patterns and group similar inputs forms the foundation of unsupervised learning.
Unsupervised learning techniques can be found in almost every foundation model (large amounts of data trained on using self-supervision), and it mainly excels in pattern discovery, clustering, and anomaly detection.
Multimodal Learning
When testing different input types such as audio or photo inputs, Rosenblatt thought of incorporating an interesting twist. He wanted to see how the perceptron would react to a combined input of both audio and visual stimuli.
He found that “by combining audio and photo inputs, it is possible to associate sounds, or auditory ‘names’ to visual objects, and to get the perceptron to perform such selective responses as are designated by the command ‘Name the object on the left,’ or ‘Name the color of this stimulus’”.
This discovery can be found in the large language models (LLMs) we use everyday. LLMs such as GPT-4o, Gemini, Claude 3, etc. are all multimodal, meaning you can input not only text but also images, audio, files, etc.
Peculiar Behaviour
Rosenblatt found that through testing, the perceptron exhibits similar behaviour to Goldstein’s brain-damaged patients (WW1 soldiers examined by Kurt Goldstein).
He found that the perceptron can learn responses to definite, concrete stimuli such as naming the colour of a stimulus if it is on the left or the shape of an object if it is on the right. However, as soon as the response calls for the recognition of a relationship between stimuli, the problem becomes excessively difficult for the perceptron.
These can be questions such as “‘Name the object left of the square’ or ‘Indicate the pattern that appeared before the circle’”. He emphasized the need for more advanced systems to enable higher-order abstraction, hinting at the future development of architectures like the Transformer.
This paper, at the time, was the closest model that could simulate the basic functions of the brain. Today, Rosenblatt’s discoveries are in virtually every machine learning model out there; without his idea of the perceptron, we would not have been able to come this far. At the end of his paper, he poses a fundamental point:
“The question may well be raised at this point of where the perceptron’s capabilities actually stop”.