Hebb’s theory was groundbreaking because it provided a biological basis for the learning processes that had previously been explained only behaviorally. It bridges the gap between psychology and neurobiology by proposing a mechanism for how experiences can lead to changes in the brain’s neural network. In practical terms, Hebbian learning explains how repeated experiences can shape brain activity and behavior. For example, when a person learns a new skill, repeated practice leads to stronger neural pathways, making the skill easier to perform over time.
Modern neuroscience and artificial intelligence have built upon Hebbian principles, particularly in the development of neural networks and machine learning algorithms. Although later research has revealed the limitations of Hebbian Theory—such as the need for mechanisms to prevent runaway excitation and to allow synaptic weakening—it remains a key concept in understanding how neural plasticity operates. Variations and extensions of the theory, such as Spike-Timing Dependent Plasticity (STDP), have refined Hebb's original ideas to incorporate the timing of neural activity in synaptic changes. Overall, Hebbian Theory continues to influence studies on brain function, learning, and memory, providing a conceptual framework for both biological and artificial systems of intelligence.