The Humble Beginning: McCulloch–Pitts Neuron #

Back in 1943, two researchers — Warren McCulloch and Walter Pitts — created one of the simplest models of a neuron. They weren’t trying to build modern AI; they were trying to understand how the brain could compute anything at all.

Their idea was beautifully simple:

• A neuron takes inputs (like little signals)
• It adds^* them together
• It compares the total to a threshold
• If the total reaches or crosses the threshold → Output = 1
• Otherwise → Output = 0

* In the basic MCP model, inputs are treated as 0/1 values, so adding them is correct. Later models introduce weights.

Let’s look at the computation of possibilities in a table below:

What do you think this is? Yes, you are right this is the logical AND operation. The trick is in the threshold and in the check.

But there is a problem: The neuron couldn’t learn. A human had to choose the threshold. Everything was hand-crafted (sum and activation). That eventually led to the next big idea: learning from data.

But before we go there, let’s make this personal.

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A Human Analogy: Decision Making in a Room Full of People #

Imagine you walk into a room where 10 people are waiting to advise you:

• 🧑‍🤝‍🧑 3 family members
• 👭 2 close friends
• 🔬 5 AI experts

You ask a simple but important question:

  Immediately, the room comes alive. Each person gives an answer — some say yes, some say no, some say “depends.” But here’s the important part:

You automatically process these opinions differently. #

Even before you consciously think:

• Family members know your responsibilities, so their words hit deeper.
• Friends know your personality, so they speak your language.
• Experts know the technical reality, so they talk with confidence.

Each person’s voice carries a different influence on your mind.

When family speaks, you may think:

“They know me best.”
→ So their voice feels heavier.

When friends speak, you think:

“They get my vibe but not always the details.”
→ Their voice has moderate influence.

When an expert speaks, you think:

“Hmm… experts talk logically, but do they know my life?”
→ Sometimes their voice stabilizes you, sometimes it scares you 😜
→ So their influence could even be negative — meaning their YES pushes you toward NO.

Let’s say

“Even before anyone talks, YES.” - This is your bias (your prejudice)

This emotional vetting is exactly what neural networks do mathematically.

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Enter Weights and Biases #

In your real life: (weights)

• Some opinions count more
• Some count less
• Some count negatively

Your prejudice (bias)

• Your bias value shifts the final decision point, just like the threshold in an MCP neuron.

In modern AI:

• Inputs are multiplied by weights
• A bias shifts the decision point (threshold)
• Learning = adjusting weights and bias based on mistakes

A machine does the same thing — just with numbers.

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Why This Matters #

Once you understand this:

• You understand how AI represents decisions
• You understand how AI starts simple and becomes powerful
• You see why learning is possible — weights change
• You see why early neurons didn’t learn — weights were fixed

This foundation will help you make sense of everything that comes next: perceptrons, activation functions, deep learning, transformers — all of them build on this idea.

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Mapping Human Intuition to Neural Network Concepts #

🏋️‍♂️ Let’s Put Numbers to Your Room #

Because this is where the magic clicks.

Let’s say you (the “neuron”) respond like this:

• Family influence = 2
  (heavy impact)

• Friends influence = 1
  (moderate impact)

• Experts influence = –1
  (their opinions sometimes drag your decision backward 😆)

You decide to keep your bias = 3, which represents:

“Even before anyone talks, I’m already slightly leaning toward YES.”
(Your personal push toward growth, curiosity, ambition.)

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🧮 **Let’s look at the computation : #

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What this shows (beautiful teaching moment) #

• Even one friend’s encouragement (0,1,0) can push you to YES
• Expert disapproval (E=1) lowers the total
• Family influence (F=1) is the strongest because its weight is 2
• Bias (3) shifts everything upward
• Threshold forces a clear YES/NO decision

This is literally how a McCulloch–Pitts neuron works.

inputs → weighted sum + bias → activation (threshold check) → output #

What Comes Next? #

In the next post, we’ll move from:
“A neuron that cannot learn” → “A neuron that learns from data.”

That neuron is called the Perceptron, and it’s the next major step in the history of AI.

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A Final Note About the “Uninitiated” #

If you’re wondering about the word uninitiated, here’s what I mean:

Someone who has not yet been introduced to a subject —
but is ready and curious to begin.

This series is my way of holding the door open for you.

This discussion is part of AI For Unitiated