Description: Synapse is the junction between two neurons, where signal transmission occurs. This process is fundamental for neuronal communication and is carried out through the release of neurotransmitters, which are chemical substances that allow the transmission of electrical impulses between neurons. Synapses can be of two types: chemical synapses, where neurotransmitters are released into the synaptic cleft and bind to receptors on the postsynaptic neuron, and electrical synapses, which allow direct signal transmission through gap junctions. Synaptic plasticity, which refers to the ability of synapses to strengthen or weaken over time, is crucial for processes such as learning and memory. In the context of neuromorphic computing, synapses serve as a model for designing circuits that mimic the brain’s functioning, aiming to replicate its efficiency and processing capacity. This approach is based on the idea that by emulating biological synapses, computational systems can be created that operate more similarly to human cognition, allowing for more efficient and adaptable information processing.
History: The term ‘synapse’ was introduced by British physiologist Charles Scott Sherrington in 1897, who used it to describe the connection between neurons. Throughout the 20th century, research on synapses expanded, especially with the development of microscopy techniques that allowed for the observation of these structures at the cellular level. In the 1950s, neurotransmitters were discovered, providing a deeper understanding of how signals are transmitted between neurons. The study of synapses has been a key area within neuroscience, contributing to the understanding of neurological and psychiatric disorders.
Uses: Synapses have applications in various areas of neuroscience, including the study of memory and learning, as well as in the research of neurodegenerative diseases such as Alzheimer’s. In neuromorphic computing, synapses are used as models to develop circuits that mimic brain behavior, which can lead to advancements in artificial intelligence and data processing.
Examples: A practical example of the application of synapses in neuromorphic computing is the use of artificial neural networks that simulate synaptic behavior for pattern recognition tasks. Additionally, some neuromorphic devices, such as Intel’s Loihi chip, utilize synaptic models to perform real-time learning tasks.
