The Biological Neuron Inspiration

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Lesson Objectives

By the end of this lesson, learners will be able to:

• Understand the basic structure and function of a biological neuron.

• Explain how information is processed and transmitted in the brain.

• Recognize the analogy between biological and artificial neurons.

• Appreciate why artificial neural networks are inspired by the brain’s architecture.

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1. What Is a Neuron?

A neuron is a specialized cell in the human brain and nervous system responsible for transmitting information using electrical and chemical signals.

Humans have around 86 billion neurons, and these interconnected cells form the basis of cognition, perception, and control of the body.

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2. Structure of a Biological Neuron

A neuron has three main components:

1. Dendrites

• Branch-like structures that receive signals from other neurons.

• Think of them as inputs.

• Signals come in the form of electrical impulses.

2. Cell Body (Soma)

• The control center of the neuron.

• Processes incoming signals from dendrites.

• If the total input signal is strong enough, it triggers an action potential.

3. Axon

• A long, cable-like structure that sends signals to other neurons.

• Ends in terminal branches connected to other neurons’ dendrites.

These connections occur at synapses—tiny gaps where chemicals called neurotransmitters pass the signal from one neuron to another.

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3. How Information Flows

Here’s a step-by-step breakdown of how a biological neuron fires:

1. Signal received by dendrites.

2. Cell body integrates signals (adds up all incoming signals).

3. If the total exceeds a threshold, an action potential is triggered.

4. The signal travels down the axon.

5. Signal reaches synaptic terminals.

6. Neurotransmitters are released into the synapse.

7. The next neuron receives the signal via its dendrites.

This is called electrochemical transmission.

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4. From Biology to Deep Learning

Artificial Neural Networks (ANNs) are not perfect replicas of the brain, but they are inspired by its structure:

Biological process:

Input (dendrites) → Process (soma) → Output (axon) → Other neurons

Artificial neuron process:

Input (x) → Multiply with weights (w) → Sum → Apply activation function → Output (y)

Mathematically:

$$y = \sigma(w_1x_1 + w_2x_2 + \dots + w_nx_n + b)$$

Where:

$$x_1, x_2, \dots, x_n = inputs$$

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$$w_1, w_2, ... w_n = weight$$

b = bias (like a threshold)

σ\sigma = activation function (like decision to fire)

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5. Why the Brain Is an Inspiration

• The brain can learn from experience — artificial networks aim to do the same.

• The brain processes massive amounts of data in parallel — ANNs try to emulate this.

• Neurons are highly interconnected — just like the layers in deep neural networks.

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6. Historical Context

• In 1943, Warren McCulloch and Walter Pitts proposed the first mathematical model of a neuron.

• In the 1950s, Frank Rosenblatt introduced the Perceptron, inspired by neurons.

• Today’s deep networks are the descendants of these early bio-inspired models.

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7. Summary

• Biological neurons process and transmit signals using dendrites, a soma, and an axon.

• Artificial neurons simplify this into inputs, a weighted sum, and an output.

• Deep learning models are built using layers of artificial neurons to mimic the brain's learning process.

• While artificial neurons are inspired by biology, they are much simpler and operate on mathematical principles.

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Questions

1. What are the three main parts of a biological neuron?

2. What is the function of dendrites?

3. What role does the soma play in signal processing?

4. What is the artificial equivalent of a synapse?

5. How is the output of an artificial neuron typically computed?

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