How do artificial neural networks resemble the brain?

Artificial neural networks are computational models inspired by the structure and function of the human brain. This approach, known as connectionism, treats the brain as a complex computing machine where neurons act as simple processors.1

Key resemblances include:

• Distributed Knowledge: Like the brain, artificial neural networks store information across the entire system rather than in a single location. Knowledge is represented by the connections between artificial neurons and the adaptive weights assigned to those connections.2
• Learning and Plasticity: Networks learn by adjusting these connection weights through exposure to data, mirroring how the brain’s neuroplasticity involves strengthening or weakening synapses based on experience.23
• Pattern Recognition: These systems emulate the brain’s ability to identify complex patterns, such as in speech or handwriting, by processing weighted inputs against specific thresholds to determine if a neuron "fires".42
• Resilience: Early research demonstrated that artificial networks could maintain performance despite the random destruction of some neurons, a feature reminiscent of the brain's ability to tolerate limited damage.4