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The Biological Basis of Memory: How does Hebbian theory reveal the secrets of learning?
In the process of exploring human learning and memory, Hebbian theory is undoubtedly a key theory. The theory was proposed by psychologist Donald Hebb in 1949 to explain synaptic plasticity - how neurons adapt during learning. The core of Hebb's theory is that when the connection between neuron A and neuron B is strengthened by frequent stimulation, this sustained activation state will lead to stronger synaptic efficiency, thus forming a long-term memory trace.
Hebb once said: "If neuron A repeatedly participates in the firing of neuron B before it fires, then some growth process or metabolic change must occur to increase the efficiency of the connection between them."
The concept of Hebbian theory can be simplified into a famous saying: "Neurons that fire simultaneously make connections." It reveals the connection mechanism in the learning process. However, the theory goes much further than that, providing a biological basis for how neurons form memories, especially in the face of multiple synergistic effects.
Hebb’s memory traces and cell assembly theory
Hebbian theory not only explains the application of a single neuron, but also covers how it works together with other neurons to form what is called a "combination of cells." Hebb once pointed out that any two or more cells or nervous systems that are continuously active for the same period of time will tend to correlate with each other, so that the activity of one promotes the activity of the other. This process of strengthening connections eventually forms memory traces, or "engrams."
Herb mentioned in his book: "When one cell repeatedly helps to excite another cell, the axon of the first cell develops a synaptic enlargement on the cell body of the second cell." This means that the learning process is also accompanied by changes in physiological structure.
This theory was tested in experiments with marine gastropods such as sea lettuce (Aplysia californica). In experiments conducted on the brains of these animals, the presence of Hebbian learning mechanisms has indeed been observed. This illustrates that in biological systems, learning is not only a cognitive process but also a process of actual physiological changes.
Relationship with unsupervised learning
Another interesting aspect of Hebbian theory is its relevance to modern unsupervised learning techniques. Since Hebbian learning relies on the coincidence of front and rear synaptic activities, this learning model can effectively capture the statistical properties of the input data, thereby achieving the effect of unsupervised learning. This makes Hebbian theory a famous cornerstone in the design of artificial neural networks.
Many scholars believe that "Hebbian learning provides theoretical support for the development of artificial neural networks. It tells us how to adjust the strength of connections between neurons based on experience."
Although Hebbian theory emphasizes the strengthening of connections between neurons that activate simultaneously, it does not encompass all forms of synaptic plasticity. For example, in the case of inhibitory synapses, the application of Hebbian theory is somewhat limited. Therefore, future research needs to further explore other types of learning mechanisms to more fully understand the complexity of learning and memory.
Conclusion
At the intersection of philosophy and science, the value of Hebbian theory is that it allows us to gain a deeper understanding of the complex biological phenomenon of learning. As neuroscience continues to develop, we are not only making further progress in understanding memory, but we are also constantly thinking about: How are real memories formed and changed in our minds?
