How do neural networks imitate some features of the human brain?

Artificial neural networks are computer programs designed to emulate the brain’s pattern-recognition capabilities by modeling the structure and function of biological neurons. They imitate the human brain through several key features:1

• Distributed Knowledge: Unlike traditional programs where instructions are explicitly written, knowledge in a neural network is distributed throughout the system. It is stored as adaptive weights assigned to the connections between artificial neurons.1
• Parallel Processing: Inspired by the brain, these networks perform computations collectively and in parallel. Rather than following step-by-step logical rules, they process information by simultaneously satisfying multiple constraints.2
• Learning through Experience: Networks learn by adjusting the weights of connections between neurons based on exposure to data. This mimics how biological systems adapt; for instance, if the total weighted input to a neuron exceeds a specific threshold, it sends a signal to connected neurons in the next layer.1
• Structural Layers: Networks are organized into input, output, and "hidden" layers, where the bulk of information processing occurs, mirroring the interconnected nature of neural pathways.1

Through these mechanisms, neural networks perform complex cognitive functions such as machine learning, speech recognition, and problem solving.1