Deep Learning

Understanding Neurons: The Brain’s Communication System

Deep learning is inspired by how your brain works. Just like how your neurons pass signals, make decisions, and get better through practice, deep learning models use artificial neurons and adjustable connections to learn from data. To understand how this works, it helps to first understand what neurons are and how they function within the brain.

Overview

What are neurons?

• A neuron is a tiny cell in your brain that sends and receives messages.

• You have billions of them, and they’re the basic building blocks of everything you think, feel, and do.

An example:

Imagine you’re playing catch with a friend. This simple activity involves millions of neurons working together. Here’s how:

1. Seeing the Ball (Input)

   1. Your eyes see the ball coming toward you.

   2. Light hits your eyes → sensory neurons carry that info to your brain.

   3. These neurons tell your brain: “A ball is flying toward you!”

2. Processing What to Do (Hidden Layers)

   1. Now your brain has to figure out: “How fast is it moving? Where will it land? Should I move my hands?”

   2. Neurons in your brain’s visual, motor, and decision-making areas talk to each other.

   3. This is where a chain of neurons pass messages like: “It’s coming to your left!” → “Get ready to catch!”

3. Moving to Catch (Output)

   1. Motor neurons send signals from your brain to your muscles:

      1. “Move your arm!”

      2. “Close your hand now!”

   2. Your muscles obey the instructions—because neurons told them to.

4. Feeling the Catch (Feedback)

   1. As your hand touches the ball, sensory neurons again fire: “Yes! I caught it!”

   2. That info goes back to your brain, helping you adjust next time.

So, what does a neuron do?

• Receives a signal from other neurons (Like when your eyes see the ball coming).

• Processes the signal: Is it strong enough? Is it important?

  • “Is this important? Is the ball close enough to act?”

• Sends out a signal if needed -> If yes, the neuron sends the message on:

  • “Move your hand now!” to the next neurons that control your muscles.

Learning happens when the strength of these connections (called synapses) changes over time based on experience.

• The more you practice catching the ball, the stronger the connections become between your “see the ball” neurons and your “move to catch” neurons.

• Over time, your reactions get faster and smoother—because your brain has literally rewired itself to get better at the task.

How Deep Neural Networks Mimics the Brain

Just like your brain uses neurons and synapses to pass messages and learn from experience, a deep learning model uses artificial neurons and weights.

• In your brain, a neuron decides whether to pass a message forward.

• In deep learning, an artificial neuron takes input, applies a weight, and decides whether to send a signal to the next layer.

And just like your brain strengthens useful connections as you practice catching a ball, a deep learning model adjusts its weights each time it sees data—so it gets better at the task over time.

In both brains and machines, learning means getting better by adjusting the connections between neurons.

An Example:

Let’s say you want to build a deep learning model that can tell whether a picture contains a cat or a dog.

What Happens Inside the Model?

1. Input Layer

   1. The image (made of pixels) is passed into the network. Each pixel becomes a number that feeds into the first layer of artificial neurons.

2. Hidden Layers

   1. Each artificial neuron takes in inputs, multiplies them by weights, adds them up, and passes the result through an activation function (which decides whether the neuron should “fire” or not).

   2. For example:

      1. “Does this pixel pattern look like a cat’s ear?”

      2. If yes, that neuron activates and sends a signal forward.

3. Output Layer

   1. The final neurons give you a result like:

      1. Cat: 0.85 (85% probability the image is a cat)

      2. Dog: 0.15 (15% probability the image is a dog).

   2. The model predicts “cat.”

How Does It Learn?

• After the prediction, the model checks if it was right.

• If it was wrong (e.g., it said “dog” but the image was a cat), it goes back and adjusts the weights of the connections between neurons.

This happens through a process called backpropagation, which tells the model how much each neuron contributed to the error, and updates the weights accordingly.

In Summary:

A deep learning algorithm works by passing data through layers of artificial neurons, using weights to control each neuron’s influence, activation functions to decide what signals to pass forward, and adjusting the weights over time to improve performance—just like the brain strengthens useful connections through learning.