Neural Networks: From Biological to Artificial

Understanding how artificial neurons mimic biological neural networks

Biological Neuron

How it works:

Dendrites receive signals from other neurons. The cell body integrates these signals. If the combined signal exceeds a threshold, the neuron fires an action potential through the axon to other neurons.

Artificial Neuron (Perceptron)

How it works:

Inputs (x₁, x₂, ...) are weighted (w₁, w₂, ...) and summed. A bias (b) is added. The result passes through an activation function to produce the output.

Key Similarities

Integration: Both biological and artificial neurons combine multiple inputs into a single output.

Threshold: Both have a threshold mechanism - biological neurons fire when membrane potential exceeds threshold, artificial neurons activate when weighted sum exceeds bias.

Non-linearity: Both introduce non-linearity - biological through action potential generation, artificial through activation functions.

Common Activation Functions
Sigmoid
σ(x) = 1 / (1 + e^(-x))
ReLU
ReLU(x) = max(0, x)
Tanh
tanh(x) = (e^x - e^(-x)) / (e^x + e^(-x))