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The mystery of Hebb's law: Why do neurons connect to each other?
In the neuroscience community, Hebbian Theory is widely accepted as an important theory to explain how connections are formed between neurons. Donald Hebb first proposed this theory in the early 1950s in his book The Organization of Behavior, stating that "neurons that fire together will wire together." This implies that the strength of the connections between neurons will change with time. Their synergistic activities are enhanced.
The core idea of Hebb's law is that if one neuron (called neuron A) frequently stimulates another neuron (called neuron B), this will lead to a spike in activity between neuron A and neuron B. Touch performance is enhanced.
This theory attempts to explain the process of "how the brain learns", especially in the context of learning and memory formation, Hebb's law becomes a key neurobiological basis. Hebb emphasized that this causal relationship can only truly occur when neuron A fires before neuron B in time, which made his methodology foreshadow the later spike-timing-dependent plasticity (STDP). )concept.
The "timing" element emphasized by Hebb's law allows us to understand that the connection between neurons will only be strengthened when the activity of neurons is properly sequenced, rather than simply relying on the concept of simultaneous activity.
Many empirical studies of Hebb's law have shown that this theory has a profound impact on revealing the process of joint learning. When different neurons are active at the same time, this phenomenon will lead to a significant increase in the strength of the synapses between them. This mechanism is closely related to our learning process and supports some seamless learning methods, especially in the fields of education and memory reconstruction.
Hebb's cell integration theory
Hebb's law is not limited to the association of single neurons, but also extends to the cell assembly theory described by Hebb. The theory holds that any two neurons or neural systems that are frequently active at the same time will strengthen their connections with each other, thereby promoting each other's activity. This concept reveals that neurons do not just interact individually, but rather form a complex interactive integration. An extension of this idea is the exploration of the formation of "learning traces" (Engrams).
Some studies have shown that when a system’s input patterns produce repetitive activity, the neurons that make up those patterns of activity increasingly strengthen their connections with one another. In this process, the combination of neurons with strengthened connections forms an automatic associative pattern, which is called a learning trace. This conclusion suggests that the learning process is not accidental but rather a structural change caused by the increased intrinsic connectivity of the organism.
The concept of autoassociation not only explains how memories are formed, but also provides an explanation for how the nervous system processes information efficiently.
Applications and Challenges of Hebb's Law
Contemporary researchers such as Eric Gandel also use the Hebbian learning principle to explore the changes in neurons and their biological mechanisms. Gandell's work focused specifically on the nervous system of marine gastropods, demonstrating the modulatory effects of Hebbian learning at the synaptic level. Although research on vertebrates faces higher challenges, the process of Hebbian learning has been confirmed in biological models.
Although the Hebbian learning principle provides a powerful explanation for the formation of associations, it still has limitations. This theory fails to adequately consider the involvement of inhibitory synapses and cannot explain anticausal spike trains. Furthermore, not only the synapses between active neurons A and B change, but also surrounding synapses may be affected, making many forms of neural plasticity non-Hebbian.
Future Research Directions
Many models of neural plasticity cannot fully cover the underlying mechanisms of Hebbian learning, which has promoted the formation of new theories, such as the BCM theory and Oja's law, to further explain the process of neural learning. In addition, as the research deepens, how to effectively integrate different learning principles will likely provide a more comprehensive perspective for our understanding of the brain's ability to explore unsupervised learning.
In future neuroscience research, can we uncover the deeper secrets of the complex connections between neurons to understand the brain's learning and memory processes?
