Language
Country/Area
No result found
Symphony of neurons: What is the magical effect of "firing together"?
In the quest to understand how the brain works, the phenomenon of "firing together" has given neuroscientists a fascinating insight into how neurons are connected to each other. This concept is derived from Hebbian theory, which was first proposed by psychologist Donald Hebb in 1949 to try to explain the plasticity of synapses between neurons and how they adapt during the learning process.
The essence of Hebb's law is: "Neurons that fire together will wire together."
Hebb's idea was that when one neuron (call it neuron A) continually stimulates another neuron (neuron B), this will cause the connection between the two to become stronger. This long-lasting stimulation strengthens the connections between neurons, leading to the emergence of higher-level cognitive functions such as learning and memory. Hebb defined this process as a permanent cellular change that increases the stability of neurons.
In this process, Hebb emphasized the importance of causality. He showed that neuron A can actually boost the activity of B only if neuron A fires before neuron B. This causal relationship laid the foundation for the current development of the understanding of timing and synaptic plasticity, especially in the study of so-called spike-timing-dependent plasticity.
Hebb pointed out that when one neuron repeatedly helps another neuron fire, it creates a lasting change between the two, making the connection stronger.
In the study of neural networks and cognitive functions, Hebb's law is considered the neural basis of unsupervised learning. Unsupervised learning itself means that the system can autonomously learn the associations between input data even without explicit guidance or labels. This makes Hebb's theory applicable not only in the field of biology, but also widely used in artificial intelligence and machine learning.
Application of Hebb's formulation
The involvement of Hebbian learning mechanisms has been demonstrated in various experiments, particularly in studies of marine terrestrial invertebrates such as the California sea slug Aplysia californica. Although it is difficult to study long-term synaptic changes in vertebrate neurons, there are still some findings showing the existence of Hebbian processes in the vertebrate brain.
Hebb's theory has wide-ranging applications, changing the biological basis of educational and memory rehabilitation methods and playing a key role in revealing cell assembly theory. Hebb believed that any pair of neurons that fired repeatedly at the same time would become linked, and that this link would persist over the long term as the activity grew stronger. This concept can help us better understand how learning forms memory traces (engrams) in neurons.
Hebb believed that "active patterns will automatically connect," meaning that the brain is able to form groups of active cells and further strengthen the connections between these cells.
Limitations of Hebbian Learning
Although Hebb's model has been quite useful in the study of long-term potentiation, it also has limitations. Hebb's law fails to explain all forms of inhibitory synapses and makes no predictions for anticausal spike trains (i.e., spikes produced by the preceding neuron after the firing of the following neuron). Furthermore, changes in synapses can occur between neighboring neurons, not just between active neurons A and B.
This situation shows that although Hebb's theory provides us with a framework for understanding neural learning and memory, we still need to explore more non-Hebbian learning processes and models to fully explain the brain's synaptic plasticity and learning adaptability. .
Concluding Thoughts
Hebb's law not only promoted the development of neuroscience, but also played a huge role in deepening our understanding of the learning and memory processes. Future research needs to not only explore the potential applications of this theory, but also conduct a deeper exploration of its limitations to promote the development of artificial intelligence and clinical applications. Is it possible that in future studies of learning and memory, new discoveries will change our understanding and application of Hebb's law?
