This project implements a basic artificial neuron component, a fundamental building block of artificial neural networks. The component provides a simple and flexible way to model neurons with customizable weights, bias, and activation functions.

• Weighted Sum and Bias: Calculates the weighted sum of input values and adds a bias term.
• Activation Function: Applies an activation function to the weighted sum to produce the neuron's output.
• Weight Updates: Supports updating weights based on a learning rate and error signal.

Using Eclipse:

1. Download the latest release: Go to the Releases page of the repository and download the latest ZIP file.

2. Import the project into Eclipse:

   • Open Eclipse and go to File > Import...
   • Select General > Existing Projects into Workspace and click Next.
   • Click Browse next to the Select root directory field and choose the downloaded ZIP file.
   • Make sure the project is checked and click Finish.

3. Run the demo:

   • Open the SimpleArtificialNeuronDemo.java file in the src folder.
   • Right-click anywhere in the file and select Run As > Java Application.

See the SimpleArtificialNeuronDemo.java file in the src folder for an example of how to use the this component.

• Multiple Activation Functions: Currently, the component only supports the sigmoid activation function. In the future, it would be beneficial to allow users to choose from a variety of activation functions, such as ReLU, tanh, Leaky ReLU, and others. This could be achieved by:
  • Creating an ActivationFunction interface with a common activate method.
  • Implementing various activation functions as concrete classes that implement the interface.
  • Allowing the SimpleArtificialNeuron to accept an ActivationFunction object and use its activate method.
• Multi-Layer Networks: Extend the component to support the creation of multi-layer neural networks, allowing for more complex and powerful models.
• Visualization: Develop visualization tools to display the neuron's structure, weights, and activation values.

This project is licensed under the MIT License.