🧠 Neural Systems in AI: Understanding the Brain of Artificial Intelligence 🐞

Artificial Intelligence (AI) has revolutionized the way we approach technology, and at its heart lies a fascinating concept: Neural Systems. These systems, inspired by the biological neural networks in human brains, form the backbone of machine learning and deep learning. Neural systems vary widely, from simple feedforward networks to sophisticated transformer models, each tailored for specific applications. Let’s dive into the types of neural systems, their applications, and key concepts with programmable examples. 🚀

1. 🌎 Artificial Neurons

An artificial neuron mimics the biological neuron, consisting of inputs, weights, a bias, and an activation function. It takes inputs, processes them, and produces an output based on the activation function.

Application:

• Basic building block for all neural networks.

Example in Python:

import numpy as np

# Define the inputs and weights
inputs = np.array([1.5, 2.0, -1.0])
weights = np.array([0.4, 0.6, 0.8])
bias = 2.0
# Calculate the output
output = np.dot(inputs, weights) + bias
print(f"Output: {output}")

2. 🔄 Feedforward Neural Networks (FNNs)