Recent neuroscience research is shedding new light on how the brain processes information and learns. Previous models have assumed that neurons operate according to uniform rules, but recent findings suggest that individual neurons can simultaneously employ different learning mechanisms, indicating their ability to multitask. 

Synaptic plasticity and its complexity 

Synaptic plasticity, or the ability of synapses to modify the strength of connections in response to a specific activity, is a key mechanism behind learning and memory. Traditionally, neurons were thought to strengthen connections with other simultaneously active neurons, according to what is known as Hebb’s rule. However, new research indicates that this process is more complex. Neurons can simultaneously apply different rules of synaptic plasticity, meaning that not all synapses in a single neuron operate according to the same rules. 

Multitasking of single neurons 

A study by researchers at Stanford University found that single neurons can simultaneously process different information using different learning mechanisms depending on the context. This finding suggests that neurons are more flexible and capable of organizing and storing complex information on their own than previously thought. 

New insights into memory organization 

Additional research indicates that neurons can reorganize their connections by forming so-called multi-synaptic boutons, which allows them to contact many other neurons simultaneously. This finding challenges the traditional theories, such as Hebb’s rule, and suggests that memory and learning are the result of more dynamic and complex processes than previously assumed. 

Implications for medicine and artificial intelligence 

Understanding the complex mechanisms of learning at the level of individual neurons has potential applications in medicine, especially in the treatment of neurological disorders such as depression and Alzheimer’s disease. In addition, these findings could inspire the development of more sophisticated artificial intelligence models that better reflect biological learning processes. 

Sources: Fast Company, Technology Networks, Narodowy Instytut Zdrowia Psychicznego, Earth.com, today.ucsd.edu