Description: Spike Time Dependent Plasticity (STDP) is a biological learning rule based on the timing of electrical signals in neurons. This form of synaptic plasticity adjusts the strength of synaptic connections based on when spikes of neuronal activity occur. In simple terms, if neuron A activates neuron B just before the latter fires an action potential, the connection between them strengthens. Conversely, if A’s activation occurs after B has fired, the connection may weaken. This mechanism is fundamental for learning and memory, as it allows neural networks to adapt and optimize their functioning in response to past experiences. STDP differs from other forms of plasticity, such as frequency-dependent plasticity, by focusing on the precise timing of neuronal events. This temporal approach reflects how the human brain processes information dynamically and efficiently, which is crucial for the development of neuromorphic computing systems that mimic the architecture and functioning of the brain. STDP is not only relevant in the biological context but is also being explored in the design of machine learning algorithms and artificial neural networks, where the synchronization of signals can enhance the performance and adaptability of systems.
