Introduction
Neural networks are computational models inspired by the structure and function of biological neurons in the brain. They form the foundation of modern artificial intelligence, enabling machines to learn patterns, recognize images, understand language, and make decisions.
This interactive paper explores how neurons work, how they connect to form networks, and how these networks learn to solve complex problems through training.
The Biological Neuron
In the human brain, neurons are specialized cells that process and transmit information through electrical and chemical signals. Each neuron receives inputs through dendrites, processes them in the cell body, and sends outputs through axons to other neurons.
This biological mechanism inspired the creation of artificial neurons, mathematical functions that take multiple weighted inputs, sum them together, and produce an output through an activation function.
Interactive Neural Network Visualization
Explore how neurons process information. Adjust the weights to see how they affect the output. Switch between a single neuron view and a multi-layer network.
Key Components
Weights
Weights determine the strength of connections between neurons. During training, these values are adjusted to minimize the difference between the network's predictions and the actual desired outputs. Larger weights amplify signals, while smaller or negative weights diminish or invert them.
Activation Functions
The activation function introduces non-linearity into the network, allowing it to learn complex patterns. The sigmoid function shown in the visualization squashes any input value into a range between 0 and 1, making it useful for binary classification tasks.
Bias
The bias term allows the neuron to shift the activation function left or right, providing additional flexibility in fitting the data. It enables the network to produce non-zero outputs even when all inputs are zero.
From Neurons to Networks
Individual neurons are limited in their computational power, but when connected in layers, they can approximate virtually any function. Deep neural networks consist of multiple hidden layers, each learning increasingly abstract representations of the input data.
The visualization above demonstrates both a single neuron (showing the detailed internal computation) and a multi-layer network (showing how information flows through the system).
Learning Process
Neural networks learn through a process called backpropagation. When the network makes a prediction, the error is calculated and propagated backward through the network. Each weight is then adjusted proportionally to how much it contributed to the error.
This iterative process of forward propagation (making predictions) and backward propagation (updating weights) continues until the network achieves satisfactory performance on the training data.