Exploring AI & Neuroscience in 2025 - Cognition and Perception

Introduction to AI and Neuroscience

The interplay between artificial intelligence (AI) and neuroscience has created a virtuous circle of innovation, where insights from one field accelerate progress in the other. For instance, neuroscience helps validate AI algorithms by showing how artificial models mimic biological processes, suggesting these methods are on the right track. Conversely, AI provides tools to simulate and analyze complex brain functions, advancing our understanding of cognition and perception.

This blog explores:

• How AI and neuroscience are influenced by each other.
• How to build a simple neural network in Python
• Real-world applications inspired by this synergy

Building a Simple Neural Network in Python

In theory, neural networks try as best to replicate the functioning of actual neurons. Below is a minimal implementation of a perceptron, the simplest neural network model.

Code Example: Perceptron in Python

import numpy as np
 
# Define a perceptron function
def perceptron(input1, input2, output):
    # Initialize weights and bias
    weights = np.random.rand(3)
    learning_rate = 0.1
    epochs = 100
 
    for _ in range(epochs):
        # Forward pass
        weighted_sum = input1 * weights[0] + input2 * weights[1] + 1 * weights[2]
        prediction = 1 if weighted_sum >= 0 else 0
        
        # Update weights
        error = output - prediction
        weights[0] += learning_rate * error * input1
        weights[1] += learning_rate * error * input2
        weights[2] += learning_rate * error * 1  # Bias update
 
    return weights
 
# Train the perceptron to learn the AND gate
trained_weights = perceptron(1, 1, 1)  # Input 1, Input 2, Expected Output
print("Trained Weights:", trained_weights)