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Seventeen-year-old student Brittany Wenger recently made headlines by creating an artificial neural network that doctors can use to help diagnose whether or not a breast tumor is malignant. Brittany’s network is especially notable in that it out-performs several commercial efforts, a feat that helped her to win the grand prize in the Google Science Fair. But what exactly is a neural network, and how can one be used to diagnose breast cancer?

Real Neural Networks
Before we talk about artificial neural networks, let’s take a look at what they’re modeled after, real neural networks, like the one in the human brain.

A nerve cell, or neuron, has three main parts. The dendrites on one end are connected to the axon on the other end via a cell body. The dendrites receive signals from a variety of sources, including sensory organs such as your eyes, ears, and skin, as well as signals from other neurons.

Neurons are very complicated and aren’t completely understood yet, but the simple explanation is that when the neuron receives strong enough signals through its dendrites, it releases neurotransmitters from its axon. These neurotransmitters cross the space (called a synapse) between cells. The amount of neurotransmitters released varies depending on a few factors, including how often a particular synapse is used. Scientists believe that repeated learning strengthens particular synapses, making them stronger producers of neurotransmitters.

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