Generic Neuron Simulator

A clean and interactive web application built with Streamlit to simulate the behavior of a single artificial neuron.

Live Demo

Access the deployed application here: Streamlit App

Purpose

This project serves as an educational tool to visualize the fundamental mathematical operations behind neural networks. It demonstrates how a neuron processes input data through weights, bias, and activation functions.

The core calculation performed is:

$$ output = f\left(\sum_{i=1}^{n} (w_i \cdot x_i) + bias\right) $$

Features

• Dynamic Inputs: Select between 1 to 10 input/weight pairs.
• Customizable Parameters: Manually adjust weights ($w$), input values ($x$), and bias ($b$).
• Activation Functions: Choose from common functions including ReLU, Sigmoid, Binary Step, and Linear (None).

Usage

1. Use the slider to define the number of inputs.
2. Enter values for the weights and input data.
3. Set the bias and select an activation function.
4. Click "Calcular la salida" to view the result.